High-resolution diffusion tensor imaging of the fornix predicts memory function in multiple sclerosis.

Background: Cognitive dysfunction is a known symptom of multiple sclerosis (MS), with memory recognized as a frequently impacted domain. Here, we used high-resolution MRI at 7 tesla to build on cross-sectional work by evaluating the longitudinal relationship of diffusion tensor imaging (DTI) measures of the fornix to episodic memory performance. Methods: A sample of 80 people with multiple sclerosis (mean age 51.9 ± 8.1 years; 24% male) underwent baseline clinical evaluation, neuropsychological assessment, and MRI. Sixty-four participants had follow-up neuropsychological testing after 1-2 years. Linear regression was used to assess the relationship of baseline imaging measures to follow-up episodic memory performance, measured using the Selective Reminding Test and Brief Visuospatial Memory Test. A reduced prediction model included cognitive function at baseline, age, sex, and disease course. Results: Radial (ß = -0.222, p < 0.026; likelihood ratio test (LRT) p < 0.018), axial (ß = -0.270, p < 0.005; LRT p < 0.003), and mean (ß = -0.242, p < 0.0139; LRT p < 0.009) diffusivity of the fornix significantly added to the model, with follow-up analysis indicating that a longer prediction interval may increase accuracy. Conclusion: These results suggest that fornix DTI has predictive value specific to memory function in MS and warrants additional investigation in the drive to develop predictors of disease progression.

So You Want to Image Myelin Using MRI: Magnetic Susceptibility Source Separation for Myelin Imaging.

In MRI, researchers have long endeavored to effectively visualize myelin distribution in the brain, a pursuit with significant implications for both scientific research and clinical applications. Over time, various methods such as myelin water imaging, magnetization transfer imaging, and relaxometric imaging have been developed, each carrying distinct advantages and limitations. Recently, an innovative technique named as magnetic susceptibility source separation has emerged, introducing a novel surrogate biomarker for myelin in the form of a diamagnetic susceptibility map. This paper comprehensively reviews this cutting-edge method, providing the fundamental concepts of magnetic susceptibility, susceptibility imaging, and the validation of the diamagnetic susceptibility map as a myelin biomarker that indirectly measures myelin content. Additionally, the paper explores essential aspects of data acquisition and processing, offering practical insights for readers. A comparison with established myelin imaging methods is also presented, and both current and prospective clinical and scientific applications are discussed to provide a holistic understanding of the technique. This work aims to serve as a foundational resource for newcomers entering this dynamic and rapidly expanding field.

Metabolic syndrome and adiposity: Risk factors for decreased myelin in cognitively healthy adults.

Metabolic syndrome (MetS) is a cluster of conditions that affects ∼25% of the global population, including excess adiposity, hyperglycemia, dyslipidemia, and elevated blood pressure. MetS is one of major risk factors not only for chronic diseases, but also for dementia and cognitive dysfunction, although the underlying mechanisms remain poorly understood. White matter is of particular interest in the context of MetS due to the metabolic vulnerability of myelin maintenance, and the accumulating evidence for the importance of the white matter in the pathophysiology of dementia. Therefore, we investigated the associations of MetS risk score and adiposity (combined body mass index and waist circumference) with myelin water fraction measured with myelin water imaging. In 90 cognitively and neurologically healthy adults (20-79 years), we found that both high MetS risk score and adiposity were correlated with lower myelin water fraction in late-myelinating prefrontal and associative fibers, controlling for age, sex, race, ethnicity, education and income. Our findings call for randomized clinical trials to establish causality between MetS, adiposity, and myelin content, and to explore the potential of weight loss and visceral adiposity reduction as means to support maintenance of myelin integrity throughout adulthood, which could open new avenues for prevention or treatment of cognitive decline and dementia.

Sandwich spatial saturation for neuromelanin-sensitive MRI: Development and multi-center trial.

Neuromelanin (NM)-sensitive MRI using a magnetization transfer (MT)-prepared T1-weighted sequence has been suggested as a tool to visualize NM contents in the brain. In this study, a new NM-sensitive imaging method, sandwichNM, is proposed by utilizing the incidental MT effects of spatial saturation RF pulses in order to generate consistent high-quality NM images using product sequences. The spatial saturation pulses are located both superior and inferior to the imaging volume, increasing MT weighting while avoiding asymmetric MT effects. When the parameters of the spatial saturation were optimized, sandwichNM reported a higher NM contrast ratio than those of conventional NM-sensitive imaging methods with matched parameters for comparability with sandwichNM (SandwichNM: 23.6 ± 5.4%; MT-prepared TSE: 20.6 ± 7.4%; MT-prepared GRE: 17.4 ± 6.0%). In a multi-vendor experiment, the sandwichNM images displayed higher means and lower standard deviations of the NM contrast ratio across subjects in all three vendors (SandwichNM vs. MT-prepared GRE; Vendor A: 28.4 ± 1.5% vs. 24.4 ± 2.8%; Vendor B: 27.2 ± 1.0% vs. 13.3 ± 1.3%; Vendor C: 27.3 ± 0.7% vs. 20.1 ± 0.9%). For each subject, the standard deviations of the NM contrast ratio across the vendors were substantially lower in SandwichNM (SandwichNM vs. MT-prepared GRE; subject 1: 1.5% vs. 8.1%, subject 2: 1.1 % vs. 5.1%, subject 3: 0.9% vs. 4.0%, subject 4: 1.1% vs. 5.3%), demonstrating consistent contrasts across the vendors. The proposed method utilizes product sequences, requiring no alteration of a sequence and, therefore, may have a wide practical utility in exploring the NM imaging.

A Pattern Dictionary Method for Anomaly Detection.

In this paper, we propose a compression-based anomaly detection method for time series and sequence data using a pattern dictionary. The proposed method is capable of learning complex patterns in a training data sequence, using these learned patterns to detect potentially anomalous patterns in a test data sequence. The proposed pattern dictionary method uses a measure of complexity of the test sequence as an anomaly score that can be used to perform stand-alone anomaly detection. We also show that when combined with a universal source coder, the proposed pattern dictionary yields a powerful atypicality detector that is equally applicable to anomaly detection. The pattern dictionary-based atypicality detector uses an anomaly score defined as the difference between the complexity of the test sequence data encoded by the trained pattern dictionary (typical) encoder and the universal (atypical) encoder, respectively. We consider two complexity measures: the number of parsed phrases in the sequence, and the length of the encoded sequence (codelength). Specializing to a particular type of universal encoder, the Tree-Structured Lempel-Ziv (LZ78), we obtain a novel non-asymptotic upper bound, in terms of the Lambert W function, on the number of distinct phrases resulting from the LZ78 parser. This non-asymptotic bound determines the range of anomaly score. As a concrete application, we illustrate the pattern dictionary framework for constructing a baseline of health against which anomalous deviations can be detected.

Multi-Domain Neumann Network with Sensitivity Maps for Parallel MRI Reconstruction.

MRI is an imaging technology that non-invasively obtains high-quality medical images for diagnosis. However, MRI has the major disadvantage of long scan times which cause patient discomfort and image artifacts. As one of the methods for reducing the long scan time of MRI, the parallel MRI method for reconstructing a high-fidelity MR image from under-sampled multi-coil k-space data is widely used. In this study, we propose a method to reconstruct a high-fidelity MR image from under-sampled multi-coil k-space data using deep-learning. The proposed multi-domain Neumann network with sensitivity maps (MDNNSM) is based on the Neumann network and uses a forward model including coil sensitivity maps for parallel MRI reconstruction. The MDNNSM consists of three main structures: the CNN-based sensitivity reconstruction block estimates coil sensitivity maps from multi-coil under-sampled k-space data; the recursive MR image reconstruction block reconstructs the MR image; and the skip connection accumulates each output and produces the final result. Experiments using the fastMRI T1-weighted brain image dataset were conducted at acceleration factors of 2, 4, and 8. Qualitative and quantitative experimental results show that the proposed MDNNSM method reconstructs MR images more accurately than other methods, including the generalized autocalibrating partially parallel acquisitions (GRAPPA) method and the original Neumann network.

Method to Minimize the Errors of AI: Quantifying and Exploiting Uncertainty of Deep Learning in Brain Tumor Segmentation.

Despite the unprecedented success of deep learning in various fields, it has been recognized that clinical diagnosis requires extra caution when applying recent deep learning techniques because false prediction can result in severe consequences. In this study, we proposed a reliable deep learning framework that could minimize incorrect segmentation by quantifying and exploiting uncertainty measures. The proposed framework demonstrated the effectiveness of a public dataset: Multimodal Brain Tumor Segmentation Challenge 2018. By using this framework, segmentation performances, particularly for small lesions, were improved. Since the segmentation of small lesions is difficult but also clinically significant, this framework could be effectively applied to the medical imaging field.

Myelin-Weighted Imaging Presents Reduced Apparent Myelin Water in Patients with Alzheimer's Disease.

The purpose of this study was to investigate myelin loss in both AD and mild cognitive impairment (MCI) patients with a new myelin water mapping technique within reasonable scan time and evaluate the clinical relevance of the apparent myelin water fraction (MWF) values by assessing the relationship between decreases in myelin water and the degree of memory decline or aging. Twenty-nine individuals were assigned to the cognitively normal (CN) elderly group, 32 participants were assigned to the MCI group, and 31 patients were assigned to the AD group. A 3D visualization of the short transverse relaxation time component (ViSTa)-gradient and spin-echo (GraSE) sequence was developed to map apparent MWF. Then, the MWF values were compared between the three participant groups and was evaluated the relationship with the degree of memory loss. The AD group showed a reduced apparent MWF compared to the CN and MCI groups. The largest AUC (area under the curve) value was in the corpus callosum and used to classify the CN and AD groups using the apparent MWF. The ViSTa-GraSE sequence can be a useful tool to map the MWF in a reasonable scan time. Combining the MWF in the corpus callosum with the detection of atrophy in the hippocampus can be valuable for group classification.

DIFFnet: Diffusion Parameter Mapping Network Generalized for Input Diffusion Gradient Schemes and b-Value.

In MRI, deep neural networks have been proposed to reconstruct diffusion model parameters. However, the inputs of the networks were designed for a specific diffusion gradient scheme (i.e., diffusion gradient directions and numbers) and a specific b-value that are the same as the training data. In this study, a new deep neural network, referred to as DIFFnet, is developed to function as a generalized reconstruction tool of the diffusion-weighted signals for various gradient schemes and b-values. For generalization, diffusion signals are normalized in a q-space and then projected and quantized, producing a matrix (Qmatrix) as an input for the network. To demonstrate the validity of this approach, DIFFnet is evaluated for diffusion tensor imaging (DIFFnetDTI) and for neurite orientation dispersion and density imaging (DIFFnetNODDI). In each model, two datasets with different gradient schemes and b-values are tested. The results demonstrate accurate reconstruction of the diffusion parameters at substantially reduced processing time (approximately 8.7 times and 2240 times faster processing time than conventional methods in DTI and NODDI, respectively; less than 4% mean normalized root-mean-square errors (NRMSE) in DTI and less than 8% in NODDI). The generalization capability of the networks was further validated using reduced numbers of diffusion signals from the datasets and a public dataset from Human Connection Project. Different from previously proposed deep neural networks, DIFFnet does not require any specific gradient scheme and b-value for its input. As a result, it can be adopted as an online reconstruction tool for various complex diffusion imaging.

Evaluation of 8-Channel Radiative Antenna Arrays for Human Head Imaging at 10.5 Tesla.

For human head magnetic resonance imaging at 10.5 tesla (T), we built an 8-channel transceiver dipole antenna array and evaluated the influence of coaxial feed cables. The influence of coaxial feed cables was evaluated in simulation and compared against a physically constructed array in terms of transmit magnetic field (B1+) and specific absorption rate (SAR) efficiency. A substantial drop (23.1% in simulation and 20.7% in experiment) in B1+ efficiency was observed with a tight coaxial feed cable setup. For the investigation of the feed location, the center-fed dipole antenna array was compared to two 8-channel end-fed arrays: monopole and sleeve antenna arrays. The simulation results with a phantom indicate that these arrays achieved ~24% higher SAR efficiency compared to the dipole antenna array. For a human head model, we observed 30.8% lower SAR efficiency with the 8-channel monopole antenna array compared to the phantom. Importantly, our simulation with the human model indicates that the sleeve antenna arrays can achieve 23.8% and 21% higher SAR efficiency compared to the dipole and monopole antenna arrays, respectively. Finally, we obtained high-resolution human cadaver images at 10.5 T with the 8-channel sleeve antenna array.

χ-separation: Magnetic susceptibility source separation toward iron and myelin mapping in the brain.

Obtaining a histological fingerprint from the in-vivo brain has been a long-standing target of magnetic resonance imaging (MRI). In particular, non-invasive imaging of iron and myelin, which are involved in normal brain functions and are histopathological hallmarks in neurodegenerative diseases, has practical utilities in neuroscience and medicine. Here, we propose a biophysical model that describes the individual contribution of paramagnetic (e.g., iron) and diamagnetic (e.g., myelin) susceptibility sources to the frequency shift and transverse relaxation of MRI signals. Using this model, we develop a method, χ-separation, that generates the voxel-wise distributions of the two sources. The method is validated using computer simulation and phantom experiments, and applied to ex-vivo and in-vivo brains. The results delineate the well-known histological features of iron and myelin in the specimen, healthy volunteers, and multiple sclerosis patients. This new technology may serve as a practical tool for exploring the microstructural information of the brain.

Quad-Contrast Imaging: Simultaneous Acquisition of Four Contrast-Weighted Images (PD-Weighted, T2-Weighted, PD-FLAIR and T2-FLAIR Images) With Synthetic T1-Weighted Image, T1- and T2-Maps.

Magnetic resonance imaging (MRI) can provide multiple contrast-weighted images using different pulse sequences and protocols. However, a long acquisition time of the images is a major challenge. To address this limitation, a new pulse sequence referred to as quad-contrast imaging is presented. The quad-contrast sequence enables the simultaneous acquisition of four contrast-weighted images (proton density (PD)-weighted, T2-weighted, PD-fluid attenuated inversion recovery (FLAIR), and T2-FLAIR), and the synthesis of T1-weighted images and T1- and T2-maps in a single scan. The scan time is less than 6 min and is further reduced to 2 min 50 s using a deep learning-based parallel imaging reconstruction. The natively acquired quad contrasts demonstrate high quality images, comparable to those from the conventional scans. The deep learning-based reconstruction successfully reconstructed highly accelerated data (acceleration factor 6), reporting smaller normalized root mean squared errors (NRMSEs) and higher structural similarities (SSIMs) than those from conventional generalized autocalibrating partially parallel acquisitions (GRAPPA)-reconstruction (mean NRMSE of 4.36% vs. 10.54% and mean SSIM of 0.990 vs. 0.953). In particular, the FLAIR contrast is natively acquired and does not suffer from lesion-like artifacts at the boundary of tissue and cerebrospinal fluid, differentiating the proposed method from synthetic imaging methods. The quad-contrast imaging method may have the potentials to be used in a clinical routine as a rapid diagnostic tool.

Contrast-enhanced MRI T1 Mapping for Quantitative Evaluation of Putative Dynamic Glymphatic Activity in the Human Brain in Sleep-Wake States.

Background Evaluation of the glymphatic system with intrathecal contrast material injection has limited clinical use. Purpose To investigate the feasibility of using serial intravenous contrast-enhanced T1 mapping in the quantitative evaluation of putative dynamic glymphatic activity in various brain regions and to demonstrate the effect of sleep on glymphatic activity in humans. Materials and Methods In this prospective study from May 2019 to February 2020, 25 healthy participants (mean age, 25 years ± 2 [standard deviation]; 15 men) underwent two cycles of MRI (day and night cycles). For each cycle, T1 maps were acquired at baseline and 0.5, 1, 1.5, 2, and 12 hours after intravenous contrast material injection. For the night cycle, participants had a normal night of sleep between 2 and 12 hours. The time (tmin) to reach the minimum T1 value (T1min), the absolute difference between baseline T1 and T1min (peak ΔT1), and the slope between two measurements at 2 and 12 hours (slope[2h-12h]) were determined from T1 value-time curves in cerebral gray matter (GM), cerebral white matter (WM), cerebellar GM, cerebellar WM, and putamen. Mixed-model analysis of variance (ANOVA), Friedman test, and repeated-measures ANOVA were used to assess the effect of sleep on slope(2h-12h) and to compare tmin and peak ΔT1 among different regions. Results The slope(2h-12h) increased from the day to night cycles in cerebral GM, cerebellar GM, and putamen (geometric mean ratio [night/day] = 1.4 [95% CI: 1.2, 1.7], 1.3 [95% CI: 1.1, 1.4], and 2.4 [95% CI: 1.6, 3.6], respectively; P = .001, P < .001, and P < .001, respectively). Median tmin values were 0.5 hour in cerebral and cerebellar GM and putamen for both cycles. Cerebellar GM had the highest mean peak ΔT1, followed by cerebral GM and putamen in both day (159 msec ± 6, 99 msec ± 4, and 62 msec ± 5, respectively) and night (152 msec ± 6, 104 msec ± 6, and 58 msec ± 4, respectively) cycles. Conclusion Clearance of a gadolinium-based contrast agent was greater after sleep compared with daytime wakefulness. These results suggest that sleep was associated with greater glymphatic clearance compared with wakefulness. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Anzai and Minoshima in this issue.

High resolution structural and functional MRI of the hippocampus in young adults with Down syndrome.

Down syndrome is the phenotypic consequence of trisomy 21, with clinical presentation including both neurodevelopmental and neurodegenerative components. Although the intellectual disability typically displayed by individuals with Down syndrome is generally global, it also involves disproportionate deficits in hippocampally-mediated cognitive processes. Hippocampal dysfunction may also relate to Alzheimer's disease-type pathology, which can appear in as early as the first decade of life and becomes universal by age 40. Using 7-tesla MRI of the brain, we present an assessment of the structure and function of the hippocampus in 34 individuals with Down syndrome (mean age 24.5 years ± 6.5) and 27 age- and sex-matched typically developing healthy controls. In addition to increased whole-brain mean cortical thickness and lateral ventricle volumes (P < 1.0 × 10-4), individuals with Down syndrome showed selective volume reductions in bilateral hippocampal subfields cornu Ammonis field 1, dentate gyrus, and tail (P < 0.005). In the group with Down syndrome, bilateral hippocampi showed widespread reductions in the strength of functional connectivity, predominately to frontal regions (P < 0.02). Age was not related to hippocampal volumes or functional connectivity measures in either group, but both groups showed similar relationships of age to whole-brain volume measures (P < 0.05). Finally, we performed an exploratory analysis of a subgroup of individuals with Down syndrome with both imaging and neuropsychological assessments. This analysis indicated that measures of spatial memory were related to mean cortical thickness, total grey matter volume and right hemisphere hippocampal subfield volumes (P < 0.02). This work provides a first demonstration of the usefulness of high-field MRI to detect subtle differences in structure and function of the hippocampus in individuals with Down syndrome, and suggests the potential for development of MRI-derived measures as surrogate markers of drug efficacy in pharmacological studies designed to investigate enhancement of cognitive function.

Radiological and Clinical Value of 7T MRI for Evaluating 3T-Visible Lesions in Pharmacoresistant Focal Epilepsies.

Objective: The recent FDA approval of the first 7T MRI scanner for clinical diagnostic use in October 2017 will likely increase the utilization of 7T for epilepsy presurgical evaluation. This study aims at accessing the radiological and clinical value of 7T in patients with pharmacoresistant focal epilepsy and 3T-visible lesions. Methods: Patients with pharmacoresistant focal epilepsy were included if they had a lesion on pre-operative standard-of-care 3T MRI and also a 7T research MRI. An epilepsy protocol was used for the acquisition of the 7T MRI. Prospective visual analysis of 7T MRI was performed by an experienced board-certified neuroradiologist and communicated to the patient management team. The clinical significance of the additional 7T findings was assessed by intracranial EEG (ICEEG) ictal onset, surgical resection, post-operative seizure outcome and histopathology. A subset of lesions were demarked with arrows for subsequent, retrospective comparison between 3T and 7T by 7 neuroradiologists using a set of quantitative scales: lesion presence, conspicuity, boundary, gray-white tissue contrast, artifacts, and the most helpful sequence for diagnosis. Conger's kappa for multiple raters was performed for chance-adjusted agreement statistics. Results: A total of 47 patients were included, with the main pathology types of focal cortical dysplasia (FCD), hippocampal sclerosis, periventricular nodular heterotopia (PVNH), tumor and polymicrogyria (PMG). 7T detected additional smaller lesions in 19% (9/47) of patients, who had extensive abnormalities such as PMG and PVNH; however, these additional findings were not necessarily epileptogenic. 3T-7T comparison by the neuroradiologist team showed that lesion conspicuity and lesion boundary were significantly better at 7T (p < 0.001), particularly for FCD, PVNH and PMG. Chance-adjusted agreement was within the fair range for lesion presence, conspicuity and boundary. Gray-white contrast was significantly improved at 7T (p < 0.001). Significantly more artifacts were encountered at 7T (p < 0.001). Significance: For patients with 3T-visible lesions, 7T MRI may better elucidate the extent of multifocal abnormalities such as PVNH and PMG, providing potential targets to improve ICEEG implantation. Patients with FCD, PVNH and PMG would likely benefit the most from 7T due to improved lesion conspicuity and boundary. Pathologies in the antero-inferior temporal regions likely benefit less due to artifacts.

DeepResp: Deep learning solution for respiration-induced B0 fluctuation artifacts in multi-slice GRE.

Respiration-induced B0 fluctuation corrupts MRI images by inducing phase errors in k-space. A few approaches such as navigator have been proposed to correct for the artifacts at the expense of sequence modification. In this study, a new deep learning method, which is referred to as DeepResp, is proposed for reducing the respiration-artifacts in multi-slice gradient echo (GRE) images. DeepResp is designed to extract the respiration-induced phase errors from a complex image using deep neural networks. Then, the network-generated phase errors are applied to the k-space data, creating an artifact-corrected image. For network training, the computer-simulated images were generated using artifact-free images and respiration data. When evaluated, both simulated images and in-vivo images of two different breathing conditions (deep breathing and natural breathing) show improvements (simulation: normalized root-mean-square error (NRMSE) from 7.8 ± 5.2% to 1.3 ± 0.6%; structural similarity (SSIM) from 0.88 ± 0.08 to 0.99 ± 0.01; ghost-to-signal-ratio (GSR) from 7.9 ± 7.2% to 0.6 ± 0.6%; deep breathing: NRMSE from 13.9 ± 4.6% to 5.8 ± 1.4%; SSIM from 0.86 ± 0.03 to 0.95 ± 0.01; GSR 20.2 ± 10.2% to 5.7 ± 2.3%; natural breathing: NRMSE from 5.2 ± 3.3% to 4.0 ± 2.5%; SSIM from 0.94 ± 0.04 to 0.97 ± 0.02; GSR 5.7 ± 5.0% to 2.8 ± 1.1%). Our approach does not require any modification of the sequence or additional hardware, and may therefore find useful applications. Furthermore, the deep neural networks extract respiration-induced phase errors, which is more interpretable and reliable than results of end-to-end trained networks.

So You Want to Image Myelin Using MRI: An Overview and Practical Guide for Myelin Water Imaging.

Myelin water imaging (MWI) is an MRI imaging biomarker for myelin. This method can generate an in vivo whole-brain myelin water fraction map in approximately 10 minutes. It has been applied in various applications including neurodegenerative disease, neurodevelopmental, and neuroplasticity studies. In this review we start with a brief introduction of myelin biology and discuss the contributions of myelin in conventional MRI contrasts. Then the MRI properties of myelin water and four different MWI methods, which are categorized as T2 -, T2 *-, T1 -, and steady-state-based MWI, are summarized. After that, we cover more practical issues such as availability, interpretation, and validation of these methods. To illustrate the utility of MWI as a clinical research tool, MWI studies for two diseases, multiple sclerosis and neuromyelitis optica, are introduced. Additional topics about imaging myelin in gray matter and non-MWI methods for myelin imaging are also included. Although technical and physiological limitations exist, MWI is a potent surrogate biomarker of myelin that carries valuable and useful information of myelin. Evidence Level: 5 Technical Efficacy: 1 J. MAGN. RESON. IMAGING 2021;53:360-373.

High-resolution functional connectivity of the default mode network in young adults with down syndrome.

Studies of resting-state functional connectivity MRI in Alzheimer's disease suggest that disease stage plays a role in functional changes of the default mode network. Individuals with the genetic disorder Down syndrome show an increased incidence of early-onset Alzheimer's-type dementia, along with early and nearly universal neuropathologic changes of Alzheimer's disease. The present study examined high-resolution functional connectivity of the default mode network in 11 young adults with Down syndrome that showed no measurable symptoms of dementia and 11 age- and sex-matched neurotypical controls. We focused on within-network connectivity of the default mode network, measured from both anterior and posterior aspects of the cingulate cortex. Sixty-eight percent of connections to the posterior cingulate and 26% to the anterior cingulate showed reduced strength in the group with Down syndrome (p < 0.01). The Down syndrome group showed increased connectivity strength from the anterior cingulate to the bilateral inferior frontal gyri and right putamen (p < 0.005). In an exploratory analysis, connectivity in the group with Down syndrome showed regional relationships to plasma measures of inflammatory markers and t-tau. In non-demented adults with Down syndrome, functional connectivity within the default mode network may be analogous to changes reported in preclinical Alzheimer's disease, and warrants further investigation as a measure of dementia risk.

Stability-Based Sparse Paradigm Free Mapping Algorithm for Deconvolution of Functional MRI Data.

Neuronal-related activity can be estimated from functional magnetic resonance imaging (fMRI) data with no knowledge of the timings of blood oxygenation level-dependent (BOLD) events by means of deconvolution with regularized least-squares. This work proposes two improvements on the deconvolution algorithm of sparse paradigm free mapping (SPFM): a new formulation that enables the estimation of neuronal events with long, sustained activity; and the implementation of a subsampling approach based on stability selection that avoids the choice of any regularization parameter. The proposed method is evaluated on real fMRI data and compared with both the original SPFM algorithm and conventional analysis with a general linear model (GLM) that is aware of the temporal model of the neuronal-related activity. We demonstrate that the novel stability-based SPFM algorithm yields activation maps with higher resemblance to the maps obtained with GLM analyses and offers improved detection of neuronal-related events over SPFM, particularly in scenarios with low contrast-to-noise ratio.

Value of 7T MRI and post-processing in patients with nonlesional 3T MRI undergoing epilepsy presurgical evaluation.

OBJECTIVE: Ultra-high-field 7-Tesla (7T) magnetic resonance imaging (MRI) offers increased signal-to-noise and contrast-to-noise ratios, which may improve visualization of cortical malformations. We aim to assess the clinical value of in vivo structural 7T MRI and its post-processing for the noninvasive identification of epileptic brain lesions in patients with pharmacoresistant epilepsy and nonlesional 3T MRI who are undergoing presurgical evaluation. METHODS: Sixty-seven patients were included who had nonlesional 3T MRI by official radiology report. Epilepsy protocols were used for the 3T and 7T acquisitions. Post-processing of the 7T T1-weighted magnetization-prepared two rapid acquisition gradient echoes sequence was performed using the morphometric analysis program (MAP) with comparison to a normal database consisting of 50 healthy controls. Review of 7T was performed by an experienced board-certified neuroradiologist and at the multimodal patient management conference. The clinical significance of 7T findings was assessed based on intracranial electroencephalography (ICEEG) ictal onset, surgery, postoperative seizure outcomes, and histopathology. RESULTS: Unaided visual review of 7T detected previously unappreciated subtle lesions in 22% (15/67). When aided by 7T MAP, the total yield increased to 43% (29/67). The location of the 7T-identified lesion was identical to or contained within the ICEEG ictal onset in 13 of 16 (81%). Complete resection of the 7T-identified lesion was associated with seizure freedom (P = .03). Histopathology of the 7T-identified lesions encountered mainly focal cortical dysplasia (FCD). 7T MAP yielded 25% more lesions (6/24) than 3T MAP, and showed improved conspicuity in 46% (11/24). SIGNIFICANCE: Our data suggest a major benefit of 7T with post-processing for detecting subtle FCD lesions for patients with pharmacoresistant epilepsy and nonlesional 3T MRI.