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Background: Understanding the nature of inflammatory responses in cats with bacterial and viral infections is essential for accurately managing the infection. This study aimed to investigate the diversities of inflammatory responses between bacterial and viral infections in cats to figure out their role in the pathophysiology of these infections., Methods: Seventy-five owned cats were included in the study. The evaluations were performed based on three groups: healthy control, bacterial infection group (those with bronchopneumonia and gastrointestinal tract and urinary tract infections) and viral infection group (21 with feline coronavirus [FCoV], 3 with feline leukaemia virus [FeLV] and 1 with feline calicivirus), each containing 25 individuals. Total and differential leukocyte counts, C-reactive protein (CRP), transforming growth factor beta (TGF-β), interleukin-6 (IL-6), tumour necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β) and interleukin-10 (IL-10) concentrations were assessed in the blood samples collected from sick and healthy animals., Results: No statistically significant difference was noted in serum TNF-α, IL-1β and IL-10 concentrations of the infected cats (p = 0.996, p = 0.160 and p = 0.930, respectively). Serum TGF-β concentration in the viral infection group was reduced compared to the healthy control (p = 0.001). In contrast, WBC count and IL-6 and CRP concentrations were increased in the cats with bronchopneumonia, gastrointestinal tract infections and urinary tract infections compared to the healthy control and viral infection groups (p = 0.001, p = 0.001 and p = 0.001, respectively)., Conclusion: This study revealed significant differences between bacterial and viral infections regarding the fashion of inflammatory responses in cats, and the relevant data will undoubtedly contribute to the management and control of feline infectious diseases, rendering the development of novel therapeutic strategies., (© 2024 The Author(s). Veterinary Medicine and Science published by John Wiley & Sons Ltd.)

Objective: The aim of the current study was to investigate affective personality traits in Alzheimer's disease, a neurodegenerative condition mainly characterized by episodic memory impairment., Method: The sample included 69 participants from 3 diagnostic categories. Twenty-five participants were diagnosed with subjective cognitive impairment (SCI), 26 participants were diagnosed with mild cognitive impairment of the amnestic type (aMCI), and the remaining 18 participants were diagnosed with early-stage Alzheimer's dementia (ADD). Diagnostic labels were given as a result of detailed neurological, neuropsychological, and neuroradiological assessment. Affective personality traits were assessed via Affective Neuroscience Personality Scales (ANPS)., Results: The only significant intergroup difference was obtained for the SEEKING subscale of ANPS. Here, ADD group scored significantly lower compared to the SCI group. The results of logistic regression analysis also indicated that SEEKING score successfully predicted early-stage ADD diagnosis., Conclusion: The results suggest that a specific personality constellation characterized by reduced investment in the outside world might be associated with Alzheimer's disease, either as a risk factor or a byproduct of the neurodegenerative process initiated by AD pathology.

Advances in the spatiotemporal resolution and field-of-view of neuroimaging tools are driving mesoscale studies for translational neuroscience. On October 10, 2023, the Center for Mesoscale Mapping (CMM) at the Massachusetts General Hospital (MGH) Athinoula A. Martinos Center for Biomedical Imaging and the Massachusetts Institute of Technology (MIT) Health Sciences Technology based Neuroimaging Training Program (NTP) hosted a symposium exploring the state-of-the-art in this rapidly growing area of research. "Mesoscale Brain Mapping: Bridging Scales and Modalities in Neuroimaging" brought together researchers who use a broad range of imaging techniques to study brain structure and function at the convergence of the microscopic and macroscopic scales. The day-long event centered on areas in which the CMM has established expertise, including the development of emerging technologies and their application to clinical translational needs and basic neuroscience questions. The in-person symposium welcomed more than 150 attendees, including 57 faculty members, 61 postdoctoral fellows, 35 students, and four industry professionals, who represented institutions at the local, regional, and international levels. The symposium also served the training goals of both the CMM and the NTP. The event content, organization, and format were planned collaboratively by the faculty and trainees. Many CMM faculty presented or participated in a panel discussion, thus contributing to the dissemination of both the technologies they have developed under the auspices of the CMM and the findings they have obtained using those technologies. NTP trainees who benefited from the symposium included those who helped to organize the symposium and/or presented posters and gave "flash" oral presentations. In addition to gaining experience from presenting their work, they had opportunities throughout the day to engage in one-on-one discussions with visiting scientists and other faculty, potentially opening the door to future collaborations. The symposium presentations provided a deep exploration of the many technological advances enabling progress in structural and functional mesoscale brain imaging. Finally, students worked closely with the presenting faculty to develop this report summarizing the content of the symposium and putting it in the broader context of the current state of the field to share with the scientific community. We note that the references cited here include conference abstracts corresponding to the symposium poster presentations., (© 2024. The Author(s).)

Purpose: To develop and evaluate a new pulse sequence for highly accelerated distortion-free diffusion MRI (dMRI) by inserting an additional echo without prolonging TR, when generalized slice dithered enhanced resolution (gSlider) radiofrequency encoding is used for volumetric acquisition., Methods: A phase-reversed interleaved multi-echo acquisition (PRIME) was developed for rapid, high-resolution, and distortion-free dMRI, which includes two echoes where the first echo is for target diffusion-weighted imaging (DWI) acquisition with high-resolution and the second echo is acquired with either 1) lower-resolution for high-fidelity field map estimation, or 2) matching resolution to enable efficient diffusion relaxometry acquisitions. The sequence was evaluated on in vivo data acquired from healthy volunteers on clinical and Connectome 2.0 scanners., Results: In vivo experiments demonstrated that 1) high in-plane acceleration (Rin-plane of 5-fold with 2D partial Fourier) was achieved using the high-fidelity field maps estimated from the second echo, which was made at a lower resolution/acceleration to increase its SNR while matching the effective echo spacing of the first readout, 2) high-resolution diffusion relaxometry parameters were estimated from dual-echo PRIME data using a white matter model of multi-TE spherical mean technique (MTE-SMT), and 3) high-fidelity mesoscale DWI at 550 um isotropic resolution could be obtained in vivo by capitalizing on the high-performance gradients of the Connectome 2.0 scanner., Conclusion: The proposed PRIME sequence enabled highly accelerated, high-resolution, and distortion-free dMRI using an additional echo without prolonging scan time when gSlider encoding is utilized.

While progressive muscle relaxation has demonstrated effectiveness in reducing anxiety, stress, and depression in general populations, its impact on caregivers of dementia patients remains understudied. This study investigated the effectiveness of Progressive Muscle Relaxation (PMR) in improving the well-being of dementia caregivers. Caregivers of individuals with dementia (n = 57) participated in a 3-month intervention, engaging in PMR thrice weekly. Participants completed assessments of depression, anxiety, stress, and caregiver burden at pre-test and post-intervention period. The intervention led to significant reductions in anxiety, stress, depression, and caregiver burden among participants. Female caregivers reported to have higher levels of anxiety and stress compared to males. Caregivers under financial stress showed high levels of distress and increased caregiver burden. These promising findings suggest the need for further research with larger, multicenter samples. Notably, PMR is a readily implementable intervention by nurses, requiring minimal equipment and offering a cost-effective approach to supporting caregiver well-being., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dilek Baykal reports administrative support, article publishing charges, equipment, drugs, or supplies, statistical analysis, travel, and writing assistance were provided by Istanbul Atlas University. Basar Bilgic reports administrative support, article publishing charges, equipment, drugs, or supplies, statistical analysis, travel, and writing assistance were provided by Istanbul University Istanbul Faculty of Medicine. Dilek Baykal reports a relationship with Istanbul Atlas University that includes: non-financial support. Basar Bilgic reports a relationship with Istanbul University Istanbul Faculty of Medicine that includes: non-financial support. The authors declare no conflict of interest related to this work. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Biomethanation represents a promising approach for biomethane production, with biofilm-based processes like trickle bed reactors (TBRs) being among the most efficient solutions. However, maintaining stable performance can be challenging, and both pure and mixed culture approaches have been applied to address this. In this study, inocula enriched with hydrogenotrophic methanogens were introduced to to TBRs as bioaugmentation strategy to assess their impacts on the process performance and microbial community dynamics. Metagenomic analysis revealed a metagenome-assembled genome belonging to the hydrogenotrophic genus Methanobacterium, which became dominant during enrichment and successfully colonized the TBR biofilm after bioaugmentation. The TBRs achieved a biogas production with > 96 % methane. The bioaugmented reactor consumed additional H 2. This may be due to microbial species utilizing CO 2 and H 2 via various CO 2 reduction pathways. Overall, implementing bioaugmentation in TBRs showed potential for establishing targeted species, although challenges remain in managing H 2 consumption and optimizing microbial interactions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Purpose: To compare the performance of multi-echo (ME) and time-division multiplexing (TDM) sequences for accelerated relaxation-diffusion MRI (rdMRI) acquisition and to examine their reliability in estimating accurate rdMRI microstructure measures., Method: The ME, TDM, and the reference single-echo (SE) sequences with six TEs were implemented using Pulseq with single-band (SB) and multi-band 2 (MB2) acceleration factors. On a diffusion phantom, the image intensities of the three sequences were compared, and the differences were quantified using the normalized RMS error (NRMSE). Shinnar-Le Roux (SLR) pulses were implemented for the SB-ME and SB-SE sequences to investigate the impact of slice profiles on ME sequences. For the in-vivo brain scan, besides the image intensity comparison and T 2 -estimates, different methods were used to assess sequence-related effects on microstructure estimation, including the relaxation diffusion imaging moment (REDIM) and the maximum-entropy relaxation diffusion distribution (MaxEnt-RDD)., Results: TDM performance was similar to the gold standard SE acquisition, whereas ME showed greater biases (3-4× larger NRMSEs for phantom, 2× for in-vivo). T 2 values obtained from TDM closely matched SE, whereas ME sequences underestimated the T 2 relaxation time. TDM provided similar diffusion and relaxation parameters as SE using REDIM, whereas SB-ME exhibited a 60% larger bias in the 2 > map and on average 3.5× larger bias in the covariance between relaxation-diffusion coefficients., Conclusion: Our analysis demonstrates that TDM provides a more accurate estimation of relaxation-diffusion measurements while accelerating the acquisitions by a factor of 2 to 3., (© 2024 International Society for Magnetic Resonance in Medicine.)

Abstract: Recent technological advancements have revolutionized routine brain magnetic resonance imaging (MRI) sequences, offering enhanced diagnostic capabilities in intracranial disease evaluation. This review explores 2 pivotal breakthrough areas: deep learning reconstruction (DLR) and quantitative MRI techniques beyond conventional structural imaging. DLR using deep neural networks facilitates accelerated imaging with improved signal-to-noise ratio and spatial resolution, enhancing image quality with short scan times. DLR focuses on supervised learning applied to clinical implementation and applications. Quantitative MRI techniques, exemplified by 2D multidynamic multiecho, 3D quantification using interleaved Look-Locker acquisition sequences with T2 preparation pulses, and magnetic resonance fingerprinting, enable precise calculation of brain-tissue parameters and further advance diagnostic accuracy and efficiency. Potential DLR instabilities and quantification and bias limitations will be discussed. This review underscores the synergistic potential of DLR and quantitative MRI, offering prospects for improved brain imaging beyond conventional methods., Competing Interests: Conflicts of interest and sources of funding: This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI22C1723) and National Research Foundation of Korea (RS-2023-00305153)., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Quantification of tissue parameters using MRI is emerging as a powerful tool in clinical diagnosis and research studies. The need for multiple long scans with different acquisition parameters prohibits quantitative MRI from reaching widespread adoption in routine clinical and research exams. Accelerated parameter mapping techniques leverage parallel imaging, signal modelling and deep learning to offer more practical quantitative MRI acquisitions. However, the achievable acceleration and the quality of maps are often limited. Joint MAPLE is a recent state-of-the-art multi-parametric and scan-specific parameter mapping technique with promising performance at high acceleration rates. It synergistically combines parallel imaging, model-based and machine learning approaches for joint mapping of T 1 , T 2 * , proton density and the field inhomogeneity. However, Joint MAPLE suffers from prohibitively long reconstruction time to estimate the maps from a multi-echo, multi-flip angle (MEMFA) dataset at high resolution in a scan-specific manner. In this work, we propose a faster version of Joint MAPLE which retains the mapping performance of the original version. Coil compression, random slice selection, parameter-specific learning rates and transfer learning are synergistically combined in the proposed framework. It speeds-up the reconstruction time up to 700 times than the original version and processes a whole-brain MEMFA dataset in 21 minutes on average, which originally requires ~260 hours for Joint MAPLE. The mapping performance of the proposed framework is ~2-fold better than the standard and the state-of-the-art evaluated reconstruction techniques on average in terms of the root mean squared error.

In recent years, in addition to its clinical importance, interest in the social-cognitive aspect of internet gaming disorder (IGD) has increased. This study aimed to investigate autistic traits, executive functions, and self-regulation abilities of patients with IGD. Eighty-seven male patients with IGD and eighty-three male healthy controls (HC) were included in the study. All patients were diagnosed with IGD as per the diagnostic criteria of Diagnostic and Statistical Manual of Mental Disorders-5. Healthy controls without any comorbid psychiatric diagnosis were recruited from the community. The Brief Rating Inventory of Executive Function (BRIEF) and the Social Responsiveness Scale (SRS) were implemented to evaluate autistic traits, executive functions, and self-regulation skills. The Beck Depression Inventory (BDI), Screen for Child Anxiety and Related Disorders and Internet Gaming Disorder Scale-Short-Form were used to evaluate psychopathology. The effect size of the impairments in executive functions and self-regulation skills was large (Cohen's d = 1.0-2.0). IGD groups had higher levels of autistic traits compared to healthy controls (d = 1.0-1.4). The differences in BDI and BRIEF scores remained significant in logistic regression analysis. Age at illness-onset, total severity of anxiety, and autistic traits were found as significant correlates of deficits in executive functions among patients with IGD. The results of this study supported the higher autistic traits and poorer executive function skills of patients with IGD. Deficits in executive functions were associated with autistic traits and younger age-onset of the illness., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: Field-to-susceptibility inversion in quantitative susceptibility mapping (QSM) is ill-posed and needs numerical stabilization through either regularization or oversampling by acquiring data at three or more object orientations. Calculation Of Susceptibility through Multiple Orientations Sampling (COSMOS) is an established oversampling approach and regarded as QSM gold standard. It achieves a well-conditioned inverse problem, requiring rotations by 0°, 60° and 120° in the yz-plane. However, this is impractical in vivo, where head rotations are typically restricted to a range of ±25°. Non-ideal sampling degrades the conditioning with residual streaking artifacts whose mitigation needs further regularization. Moreover, susceptibility anisotropy in white matter is not considered in the COSMOS model, which may introduce additional bias. The current work presents a thorough investigation of these effects in primate brain., Methods: Gradient-recalled echo (GRE) data of an entire fixed chimpanzee brain were acquired at 7 T (350 μm resolution, 10 orientations) including ideal COSMOS sampling and realistic rotations in vivo. Comparisons of the results included ideal COSMOS, in-vivo feasible acquisitions with 3-8 orientations and single-orientation iLSQR QSM., Results: In-vivo feasible and optimal COSMOS yielded high-quality susceptibility maps with increased SNR resulting from averaging multiple acquisitions. COSMOS reconstructions from non-ideal rotations about a single axis required additional L2-regularization to mitigate residual streaking artifacts., Conclusion: In view of unconsidered anisotropy effects, added complexity of the reconstruction, and the general challenge of multi-orientation acquisitions, advantages of sub-optimal COSMOS schemes over regularized single-orientation QSM appear limited in in-vivo settings., Competing Interests: Declaration of competing interest The authors declare no competing interest., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Purpose: To reduce the inter-scanner variability of diffusion MRI (dMRI) measures between scanners from different vendors by developing a vendor-neutral dMRI pulse sequence using the open-source vendor-agnostic Pulseq platform., Methods: We implemented a standard EPI based dMRI sequence in Pulseq. We tested it on two clinical scanners from different vendors (Siemens Prisma and GE Premier), systematically evaluating and comparing the within- and inter-scanner variability across the vendors, using both the vendor-provided and Pulseq dMRI sequences. Assessments covered both a diffusion phantom and three human subjects, using standard error (SE) and Lin's concordance correlation to measure the repeatability and reproducibility of standard DTI metrics including fractional anisotropy (FA) and mean diffusivity (MD)., Results: Identical dMRI sequences were executed on both scanners using Pulseq. On the phantom, the Pulseq sequence showed more than a 2.5× reduction in SE (variability) across Siemens and GE scanners. Furthermore, Pulseq sequences exhibited markedly reduced SE in-vivo, maintaining scan-rescan repeatability while delivering lower variability in FA and MD (more than 50% reduction in cortical/subcortical regions) compared to vendor-provided sequences., Conclusion: The Pulseq diffusion sequence reduces the cross-scanner variability for both phantom and in-vivo data, which will benefit multi-center neuroimaging studies and improve the reproducibility of neuroimaging studies., (© 2024 International Society for Magnetic Resonance in Medicine.)

Purpose: To compare the performance of multi-echo (ME) and time-division multiplexing (TDM) sequences for accelerated relaxation-diffusion MRI (rdMRI) acquisition and to examine their reliability in estimating accurate rdMRI microstructure measures., Method: The ME, TDM, and the reference single-echo (SE) sequences with six echo times (TE) were implemented using Pulseq with single-band (SB-) and multi-band 2 (MB2-) acceleration factors. On a diffusion phantom, the image intensities of the three sequences were compared, and the differences were quantified using the normalized root mean squared error (NRMSE). For the in-vivo brain scan, besides the image intensity comparison and T2-estimates, different methods were used to assess sequence-related effects on microstructure estimation, including the relaxation diffusion imaging moment (REDIM) and the maximum-entropy relaxation diffusion distribution (MaxEnt-RDD)., Results: TDM performance was similar to the gold standard SE acquisition, whereas ME showed greater biases (3-4× larger NRMSEs for phantom, 2× for in-vivo). T2 values obtained from TDM closely matched SE, whereas ME sequences underestimated the T2 relaxation time. TDM provided similar diffusion and relaxation parameters as SE using REDIM, whereas SB-ME exhibited a 60% larger bias in the 2 > map and on average 3.5× larger bias in the covariance between relaxation-diffusion coefficients., Conclusion: Our analysis demonstrates that TDM provides a more accurate estimation of relaxation-diffusion measurements while accelerating the acquisitions by a factor of 2 to 3.

Diffusion magnetic resonance imaging is an important tool for mapping tissue microstructure and structural connectivity non-invasively in the in vivo human brain. Numerous diffusion signal models are proposed to quantify microstructural properties. Nonetheless, accurate estimation of model parameters is computationally expensive and impeded by image noise. Supervised deep learning-based estimation approaches exhibit efficiency and superior performance but require additional training data and may be not generalizable. A new DIffusion Model OptimizatioN framework using physics-informed and self-supervised Deep learning entitled "DIMOND" is proposed to address this problem. DIMOND employs a neural network to map input image data to model parameters and optimizes the network by minimizing the difference between the input acquired data and synthetic data generated via the diffusion model parametrized by network outputs. DIMOND produces accurate diffusion tensor imaging results and is generalizable across subjects and datasets. Moreover, DIMOND outperforms conventional methods for fitting sophisticated microstructural models including the kurtosis and NODDI model. Importantly, DIMOND reduces NODDI model fitting time from hours to minutes, or seconds by leveraging transfer learning. In summary, the self-supervised manner, high efficacy, and efficiency of DIMOND increase the practical feasibility and adoption of microstructure and connectivity mapping in clinical and neuroscientific applications., (© 2024 The Authors. Advanced Science published by Wiley‐VCH GmbH.)

Background: Transsphenoidal endoscopic surgery is the first-line treatment for growth hormone-secreting adenomas., Objective: To analyse the results of the transsphenoidal endoscopic approach for acromegaly and to determine the predictive factors of remission., Methods: A single-centre retrospective review was performed in patients who underwent endoscopic transsphenoidal surgery for acromegaly between January 2009 and January 2019. Demographic features, clinical presentation, histopathology records, complications and pre- and postoperative radiologic and endocrinological assessments were evaluated. The factors that influenced the remission rates were investigated., Results: A total of 73 patients underwent surgery via the transsphenoidal endoscopic approach. Cavernous sinus invasion was detected in 32 patients (43.8%); and macroadenoma, in 57 (78%). The pathology specimens of the 27 patients (36.9%) showed dual-staining adenomas with prolactin. A total of 51 patients (69.8%) attained biochemical remission 1 year after surgery. A second operation was performed in 10 patients (13.6%) with residual tumours without biochemical remission in the first year. Six (60%) of the patients attained remission at the last follow-up. Transient diabetes insipidus was observed in 18 patients (24.6%); and rhinorrhoea, which was resolved with conservative treatment, in 4 (5.4%). None of the patients developed panhypopituitarism. The presence of cavernous sinus invasion and preoperative IGF-1, immediate postoperative GH and third-month IGF-1 levels were predictive of remission., Conclusion: Transsphenoidal endoscopic surgery is a safe and effective treatment for acromegaly. Reoperation should be considered in patients with residual tumours without remission.

Purpose: To develop and evaluate methods for (1) reconstructing 3D-quantification using an interleaved Look-Locker acquisition sequence with T 2 preparation pulse (3D-QALAS) time-series images using a low-rank subspace method, which enables accurate and rapid T 1 and T 2 mapping, and (2) improving the fidelity of subspace QALAS by combining scan-specific deep-learning-based reconstruction and subspace modeling., Theory and Methods: A low-rank subspace method for 3D-QALAS (i.e., subspace QALAS) and zero-shot deep-learning subspace method (i.e., Zero-DeepSub) were proposed for rapid and high fidelity T 1 and T 2 mapping and time-resolved imaging using 3D-QALAS. Using an ISMRM/NIST system phantom, the accuracy and reproducibility of the T 1 and T 2 maps estimated using the proposed methods were evaluated by comparing them with reference techniques. The reconstruction performance of the proposed subspace QALAS using Zero-DeepSub was evaluated in vivo and compared with conventional QALAS at high reduction factors of up to nine-fold., Results: Phantom experiments showed that subspace QALAS had good linearity with respect to the reference methods while reducing biases and improving precision compared to conventional QALAS, especially for T 2 maps. Moreover, in vivo results demonstrated that subspace QALAS had better g-factor maps and could reduce voxel blurring, noise, and artifacts compared to conventional QALAS and showed robust performance at up to nine-fold acceleration with Zero-DeepSub, which enabled whole-brain T 1 , T 2 , and PD mapping at 1 mm isotropic resolution within 2 min of scan time., Conclusion: The proposed subspace QALAS along with Zero-DeepSub enabled high fidelity and rapid whole-brain multiparametric quantification and time-resolved imaging., (© 2024 International Society for Magnetic Resonance in Medicine.)

Purpose: To evaluate a vendor-agnostic multiparametric mapping scheme based on 3D quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) for whole-brain T1, T2, and proton density (PD) mapping., Methods: This prospective, multi-institutional study was conducted between September 2021 and February 2022 using five different 3T systems from four prominent MRI vendors. The accuracy of this technique was evaluated using a standardized MRI system phantom. Intra-scanner repeatability and inter-vendor reproducibility of T1, T2, and PD values were evaluated in 10 healthy volunteers (6 men; mean age ± SD, 28.0 ± 5.6 y) who underwent scan-rescan sessions on each scanner (total scans = 100). To evaluate the feasibility of 3D-QALAS, nine patients with multiple sclerosis (nine women; mean age ± SD, 48.2 ± 11.5 y) underwent imaging examination on two 3T MRI systems from different manufacturers., Results: Quantitative maps obtained with 3D-QALAS showed high linearity (R 2  = 0.998 and 0.998 for T1 and T2, respectively) with respect to reference measurements. The mean intra-scanner coefficients of variation for each scanner and structure ranged from 0.4% to 2.6%. The mean structure-wise test-retest repeatabilities were 1.6%, 1.1%, and 0.7% for T1, T2, and PD, respectively. Overall, high inter-vendor reproducibility was observed for all parameter maps and all structure measurements, including white matter lesions in patients with multiple sclerosis., Conclusion: The vendor-agnostic multiparametric mapping technique 3D-QALAS provided reproducible measurements of T1, T2, and PD for human tissues within a typical physiological range using 3T scanners from four different MRI manufacturers., (© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

This article provides recommendations for implementing QSM for clinical brain research. It is a consensus of the International Society of Magnetic Resonance in Medicine, Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available have generated a need in the neuroimaging community for guidelines on implementation. This article outlines considerations and implementation recommendations for QSM data acquisition, processing, analysis, and publication. We recommend that data be acquired using a monopolar 3D multi-echo gradient echo (GRE) sequence and that phase images be saved and exported in Digital Imaging and Communications in Medicine (DICOM) format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background field removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields within the brain mask should be removed using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of the whole brain as a region of interest in the analysis. The minimum acquisition and processing details required when reporting QSM results are also provided. These recommendations should facilitate clinical QSM research and promote harmonized data acquisition, analysis, and reporting., (© 2024 International Society for Magnetic Resonance in Medicine.)

Background: Dystonia is one of the most common movement disorders. To date, the genetic causes of dystonia in populations of European descent have been extensively studied. However, other populations, particularly those from the Middle East, have not been adequately studied. The purpose of this study is to discover the genetic basis of dystonia in a clinically and genetically well-characterised dystonia cohort from Turkey, which harbours poorly studied populations., Methods: Exome sequencing analysis was performed in 42 Turkish dystonia families. Using co-expression network (CEN) analysis, identified candidate genes were interrogated for the networks including known dystonia-associated genes and genes further associated with the protein-protein interaction, animal model-based characteristics and clinical findings., Results: We identified potentially disease-causing variants in the established dystonia genes ( PRKRA, SGCE, KMT2B, SLC2A1, GCH1, THAP1, HPCA, TSPOAP1, AOPEP ; n=11 families (26%)), in the uncommon forms of dystonia-associated genes ( PCCB, CACNA1A, ALDH5A1, PRKN ; n=4 families (10%)) and in the candidate genes prioritised based on the pathogenicity of the variants and CEN-based analyses (n=11 families (21%)). The diagnostic yield was found to be 36%. Several pathways and gene ontologies implicated in immune system, transcription, metabolic pathways, endosomal-lysosomal and neurodevelopmental mechanisms were over-represented in our CEN analysis., Conclusions: Here, using a structured approach, we have characterised a clinically and genetically well-defined dystonia cohort from Turkey, where dystonia has not been widely studied, and provided an uncovered genetic basis, which will facilitate diagnostic dystonia research., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Background and Purpose: The use of MR imaging in emergency settings has been limited by availability, long scan times, and sensitivity to motion. This study assessed the diagnostic performance of an ultrafast brain MR imaging protocol for evaluation of acute intracranial pathology in the emergency department and inpatient settings., Materials and Methods: Sixty-six adult patients who underwent brain MR imaging in the emergency department and inpatient settings were included in the study. All patients underwent both the reference and the ultrafast brain MR protocols. Both brain MR imaging protocols consisted of T1-weighted, T2/T2*-weighted, FLAIR, and DWI sequences. The ultrafast MR images were reconstructed by using a machine-learning assisted framework. All images were reviewed by 2 blinded neuroradiologists., Results: The average acquisition time was 2.1 minutes for the ultrafast brain MR protocol and 10 minutes for the reference brain MR protocol. There was 98.5% agreement on the main clinical diagnosis between the 2 protocols. In head-to-head comparison, the reference protocol was preferred in terms of image noise and geometric distortion ( P  < .05 for both). The ultrafast ms-EPI protocol was preferred over the reference protocol in terms of reduced motion artifacts ( P  < .01). Overall diagnostic quality was not significantly different between the 2 protocols ( P  > .05)., Conclusions: The ultrafast brain MR imaging protocol provides high accuracy for evaluating acute pathology while only requiring a fraction of the scan time. Although there was greater image noise and geometric distortion on the ultrafast brain MR protocol images, there was significant reduction in motion artifacts with similar overall diagnostic quality between the 2 protocols., (© 2024 by American Journal of Neuroradiology.)

Purpose: Volumetric, high-resolution, quantitative mapping of brain-tissue relaxation properties is hindered by long acquisition times and SNR challenges. This study combines time-efficient wave-controlled aliasing in parallel imaging (wave-CAIPI) readouts with the 3D quantification using an interleaved Look-Locker acquisition sequence with a T 2 preparation pulse (3D-QALAS), enabling full-brain quantitative T 1 , T 2 , and proton density (PD) maps at 1.15-mm 3 isotropic voxels in 3 min., Methods: Wave-CAIPI readouts were embedded in the standard 3D-QALAS encoding scheme, enabling full-brain quantitative parameter maps (T 1 , T 2 , and PD) at acceleration factors of R = 3 × 2 with minimum SNR loss due to g-factor penalties. The quantitative parameter maps were estimated using a dictionary-based mapping algorithm incorporating inversion efficiency and B 1 -field inhomogeneity effects. The parameter maps using the accelerated protocol were quantitatively compared with those obtained from the conventional 3D-QALAS sequence using GRAPPA acceleration of R = 2 in the ISMRM/NIST phantom, and in 10 healthy volunteers., Results: When tested in both the ISMRM/NIST phantom and 10 healthy volunteers, the quantitative maps using the accelerated protocol showed excellent agreement against those obtained from conventional 3D-QALAS at R GRAPPA  = 2., Conclusion: Three-dimensional QALAS enhanced with wave-CAIPI readouts enables time-efficient, full-brain quantitative T 1 , T 2 , and PD mapping at 1.15 mm 3 in 3 min at R = 3 × 2 acceleration. The quantitative maps obtained from the accelerated wave-CAIPI 3D-QALAS protocol showed very similar values to those from the standard 3D-QALAS (R = 2) protocol, alluding to the robustness and reliability of the proposed method., (© 2023 International Society for Magnetic Resonance in Medicine.)

Purpose: To investigate whether spatiotemporal magnetic field monitoring can correct pronounced eddy current-induced artifacts incurred by strong diffusion-sensitizing gradients up to 300 mT/m used in high b-value diffusion-weighted (DW) EPI., Methods: A dynamic field camera equipped with 16 1 H NMR field probes was first used to characterize field perturbations caused by residual eddy currents from diffusion gradients waveforms in a 3D multi-shot EPI sequence on a 3T Connectom scanner for different gradient strengths (up to 300 mT/m), diffusion directions, and shots. The efficacy of dynamic field monitoring-based image reconstruction was demonstrated on high-gradient strength, submillimeter resolution whole-brain ex vivo diffusion MRI. A 3D multi-shot image reconstruction framework was developed that incorporated the nonlinear phase evolution measured with the dynamic field camera., Results: Phase perturbations in the readout induced by residual eddy currents from strong diffusion gradients are highly nonlinear in space and time, vary among diffusion directions, and interfere significantly with the image encoding gradients, changing the k-space trajectory. During the readout, phase modulations between odd and even EPI echoes become non-static and diffusion encoding direction-dependent. Superior reduction of ghosting and geometric distortion was achieved with dynamic field monitoring compared to ghosting reduction approaches such as navigator- and structured low-rank-based methods or MUSE followed by image-based distortion correction with the FSL tool "eddy.", Conclusion: Strong eddy current artifacts characteristic of high-gradient strength DW-EPI can be well corrected with dynamic field monitoring-based image reconstruction., (© 2023 International Society for Magnetic Resonance in Medicine.)

Purpose: A 128-channel receive-only array for brain imaging at 7 T was simulated, designed, constructed, and tested within a high-performance head gradient designed for high-resolution functional imaging., Methods: The coil used a tight-fitting helmet geometry populated with 128 loop elements and preamplifiers to fit into a 39 cm diameter space inside a built-in gradient. The signal-to-noise ratio (SNR) and parallel imaging performance (1/g) were measured in vivo and simulated using electromagnetic modeling. The histogram of 1/g factors was analyzed to assess the range of performance. The array's performance was compared to the industry-standard 32-channel receive array and a 64-channel research array., Results: It was possible to construct the 128-channel array with body noise-dominated loops producing an average noise correlation of 5.4%. Measurements showed increased sensitivity compared with the 32-channel and 64-channel array through a combination of higher intrinsic SNR and g-factor improvements. For unaccelerated imaging, the 128-channel array showed SNR gains of 17.6% and 9.3% compared to the 32-channel and 64-channel array, respectively, at the center of the brain and 42% and 18% higher SNR in the peripheral brain regions including the cortex. For R = 5 accelerated imaging, these gains were 44.2% and 24.3% at the brain center and 86.7% and 48.7% in the cortex. The 1/g-factor histograms show both an improved mean and a tighter distribution by increasing the channel count, with both effects becoming more pronounced at higher accelerations., Conclusion: The experimental results confirm that increasing the channel count to 128 channels is beneficial for 7T brain imaging, both for increasing SNR in peripheral brain regions and for accelerated imaging., (© 2023 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: To develop and evaluate a method for rapid estimation of multiparametric T 1 , T 2 , proton density, and inversion efficiency maps from 3D-quantification using an interleaved Look-Locker acquisition sequence with T 2 preparation pulse (3D-QALAS) measurements using self-supervised learning (SSL) without the need for an external dictionary., Methods: An SSL-based QALAS mapping method (SSL-QALAS) was developed for rapid and dictionary-free estimation of multiparametric maps from 3D-QALAS measurements. The accuracy of the reconstructed quantitative maps using dictionary matching and SSL-QALAS was evaluated by comparing the estimated T 1 and T 2 values with those obtained from the reference methods on an International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom. The SSL-QALAS and the dictionary-matching methods were also compared in vivo, and generalizability was evaluated by comparing the scan-specific, pre-trained, and transfer learning models., Results: Phantom experiments showed that both the dictionary-matching and SSL-QALAS methods produced T 1 and T 2 estimates that had a strong linear agreement with the reference values in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom. Further, SSL-QALAS showed similar performance with dictionary matching in reconstructing the T 1 , T 2 , proton density, and inversion efficiency maps on in vivo data. Rapid reconstruction of multiparametric maps was enabled by inferring the data using a pre-trained SSL-QALAS model within 10 s. Fast scan-specific tuning was also demonstrated by fine-tuning the pre-trained model with the target subject's data within 15 min., Conclusion: The proposed SSL-QALAS method enabled rapid reconstruction of multiparametric maps from 3D-QALAS measurements without an external dictionary or labeled ground-truth training data., (© 2023 International Society for Magnetic Resonance in Medicine.)

Purpose: The aim of this study is to review the clinical and laboratory characteristics, diagnostic and treatment modalities of tumor-induced osteomalacia (TIO) cases managed in a single center., Material Methods: Demographic and clinical features, biochemical findings, diagnostic procedures, treatment modalities, and outcomes of nine patients who had the diagnosis of TIO were reviewed retrospectively., Results: Mean age of the study group (F/M: 4/5) was 45.8 ± 10.8 years, and mean time from the onset of symptoms to diagnosis was 4.7 ± 2.8 years. The clinical manifestations were muscle weakness and difficulty in walking (8/9), hip pain (3/9), multiple fractures (2/9), stress fracture (2/9). Mean plasma phosphorus concentration was 1.28 ± 0.4 mg/dl at presentation. We performed radionuclide imaging modalities (18F-FDG PET/CT, Ga68-DOTATATE PET/CT, octreotide scintigraphy) in seven of nine patients, and tumor was detected in all. Lower extremity (n = 6; %67), head region (n = 2; %22) and thorax (n = 1; %11) were the tumor locations of our cases. Eight patients underwent surgery and remission was achieved postoperatively in all of the operated patients and plasma phosphorus level normalized in 4 ± 2 days. Pathological examination revealed mesenchymal tumors with different subtypes. Recurrence occurred in three patients at 13 ± 10.5 months after the first surgery. Two patients were reoperated and radiotherapy was also performed in one of them., Conclusion: Hypophosphatemia necessitates careful evaluation for the etiology. TIO is one of the important causes of adult-onset hypophosphatemic osteomalacia. Diagnosis of TIO is essential because the laboratory and clinical findings resolve after appropriate treatment., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The spread pattern of progressive degeneration seen in Alzheimer's disease (AD) to small-scale medial temporal lobe subregions is critical for early diagnosis. In this context, it was aimed to examine the morphometric changes of the hippocampal subfields, amygdala nuclei, entorhinal cortex (ERC), and parahippocampal cortex (PHC) using MRI. MRI data of patients diagnosed with 20 Alzheimer's disease dementia (ADD), 30 amnestic mild cognitive impairment (aMCI), and 30 subjective cognitive impairment (SCI) without demographic differences were used. Segmentation and parcellation were performed using FreeSurfer. The segmentation process obtained volume values of 12 hippocampal subfields and 9 amygdala nuclei. Thickness values of ERC and PHC were calculated with the parcellation process. ANCOVA was performed using age, education and gender as covariates to evaluate the intergroup differences. Linear discriminant analysis was used to investigate whether atrophy predicted groups at an early stage. ERC and PHC thickness decreased significantly throughout the disease continuum, while only ERC was affected in the early stage. When the hippocampal and amygdala subfields were compared volumetrically, significant differences were found in the amygdala between the SCI and aMCI groups. In the early period, only volume reduction in the anterior amygdaloid area of the amygdala nuclei exceeded the significance threshold. Research on AD primarily focuses on original hippocampocentric structures and their main function which is episodic memory. Our results emphasized the significance of so far relatively neglected olfactocentric structures and their functions, such as smell and social cognition in the pre-dementia stages of the AD process., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Gilles de la Tourette syndrome (GTS) is a neuropsychiatric movement disorder with reported abnormalities in various neurotransmitter systems. Considering the integral role of iron in neurotransmitter synthesis and transport, it is hypothesized that iron exhibits a role in GTS pathophysiology. As a surrogate measure of brain iron, quantitative susceptibility mapping (QSM) was performed in 28 patients with GTS and 26 matched controls. Significant susceptibility reductions in the patients, consistent with reduced local iron content, were obtained in subcortical regions known to be implicated in GTS. Regression analysis revealed a significant negative association of tic scores and striatal susceptibility. To interrogate genetic mechanisms that may drive these reductions, spatially specific relationships between susceptibility and gene-expression patterns from the Allen Human Brain Atlas were assessed. Correlations in the striatum were enriched for excitatory, inhibitory, and modulatory neurochemical signaling mechanisms in the motor regions, mitochondrial processes driving ATP production and iron‑sulfur cluster biogenesis in the executive subdivision, and phosphorylation-related mechanisms affecting receptor expression and long-term potentiation in the limbic subdivision. This link between susceptibility reductions and normative transcriptional profiles suggests that disruptions in iron regulatory mechanisms are involved in GTS pathophysiology and may lead to pervasive abnormalities in mechanisms regulated by iron-containing enzymes., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: To improve time-resolved reconstructions by training auto-encoders to learn compact representations of Bloch-simulated signal evolution and inserting the decoder into the forward model., Methods: Building on model-based nonlinear and linear subspace techniques, we train auto-encoders on dictionaries of simulated signal evolution to learn compact, nonlinear, latent representations. The proposed latent signal model framework inserts the decoder portion of the auto-encoder into the forward model and directly reconstructs the latent representation. Latent signal models essentially serve as a proxy for fast and feasible differentiation through the Bloch equations used to simulate signal. This work performs experiments in the context of T 2 -shuffling, gradient echo EPTI, and MPRAGE-shuffling. We compare how efficiently auto-encoders represent signal evolution in comparison to linear subspaces. Simulation and in vivo experiments then evaluate if reducing degrees of freedom by incorporating our proxy for the Bloch equations, the decoder portion of the auto-encoder, into the forward model improves reconstructions in comparison to subspace constraints., Results: An auto-encoder with 1 real latent variable represents single-tissue fast spin echo, EPTI, and MPRAGE signal evolution to within 0.15% normalized RMS error, enabling reconstruction problems with 3 degrees of freedom per voxel (real latent variable + complex scaling) in comparison to linear models with 4-8 degrees of freedom per voxel. In simulated/in vivo T 2 -shuffling and in vivo EPTI experiments, the proposed framework achieves consistent quantitative normalized RMS error improvement over linear approaches. From qualitative evaluation, the proposed approach yields images with reduced blurring and noise amplification in MPRAGE-shuffling experiments., Conclusion: Directly solving for nonlinear latent representations of signal evolution improves time-resolved MRI reconstructions., (© 2023 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: To develop a high-fidelity diffusion MRI acquisition and reconstruction framework with reduced echo-train-length for less T 2 * image blurring compared to typical highly accelerated echo-planar imaging (EPI) acquisitions at sub-millimeter isotropic resolution., Methods: We first proposed a circular-EPI trajectory with partial Fourier sampling on both the readout and phase-encoding directions to minimize the echo-train-length and echo time. We then utilized this trajectory in an interleaved two-shot EPI acquisition with reversed phase-encoding polarity, to aid in the correction of off-resonance-induced image distortions and provide complementary k-space coverage in the missing partial Fourier regions. Using model-based reconstruction with structured low-rank constraint and smooth phase prior, we corrected the shot-to-shot phase variations across the two shots and recover the missing k-space data. Finally, we combined the proposed acquisition/reconstruction framework with an SNR-efficient RF-encoded simultaneous multi-slab technique, termed gSlider, to achieve high-fidelity 720 µm and 500 µm isotropic resolution in-vivo diffusion MRI., Results: Both simulation and in-vivo results demonstrate the effectiveness of the proposed acquisition and reconstruction framework to provide distortion-corrected diffusion imaging at the mesoscale with markedly reduced T 2 *-blurring. The in-vivo results of 720 µm and 500 µm datasets show high-fidelity diffusion images with reduced image blurring and echo time using the proposed approaches., Conclusions: The proposed method provides high-quality distortion-corrected diffusion-weighted images with ∼40% reduction in the echo-train-length and T 2 * blurring at 500µm-isotropic-resolution compared to standard multi-shot EPI., Competing Interests: Declaration of Competing Interest The authors report no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Introduction: The number of people diagnosed with dementia is increasing, creating significant economic burden globally. With the progression of the disease, patients need a caregiver whose wellbeing is important for continuous care. Providing respite as a service, through sharing the responsibility of caregiving or support for the caregiver, is a costly initiative. A peer-to-peer online support platform for dementia caregivers, motivated by the sharing economy, putting exchange of knowhow, resources, and services at its center, has the potential to balance cost concerns with a search for respite. The aim of this research is to assess caregivers' intention to engage in peer-to-peer exchange., Methods: A survey including sociodemographic, technology use, and caregiving variables, structured questionnaires (Zarit caregiver burden, WHO brief quality of life scale, ADCS-ADL and chronic stress scale) were administered, January 2018-May 2019, in the dementia outpatient clinic of a university hospital, to a convenience sample of n  = 203 individuals identifying themselves as primary caregivers. A path analysis exploring the drivers of an intention to engage in peer-to-peer service exchange was conducted., Results: In the path model, caregivers experiencing higher caregiver burden showed higher intention to engage (0.079, p  < 0.001). Disease stage had no effect while patient activities of daily living, chronic social role related stressors of the caregiver and general quality of life were significant for the effect on the caregiver burden. Existing household support decreased the caregiver burden, affecting the intention to engage. Caregivers who can share more know-how demonstrate a higher intention to engage (0.579, p  = 0.021). Caregiver technology affinity (0.458, p  = 0.004) and ability and openness to seek professional help for psychological diagnoses (1.595, p  = 0.012) also increased intention to engage., Conclusion: The model shows caregiver burden to be a major driver, along with caregiver characteristics that reflect their technology affinity and openness to the idea of general reciprocity. Existing support for obtaining knowhow and exchanging empathy have a direct effect on the intention to engage. Given the scarcity of caregiver support in the formal care channels, the identified potential of enlarging informal support via a peer-to-peer exchange mechanism holds promise., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Aksin, Bilgic, Guner, Gunes, Kuscu, Ormeci, Sayin and Eser.)

Introduction: The key feature that distinguishes mild cognitive impairment (MCI) from dementia is the absence of significant functional decline because of cognitive impairment. In Parkinson's disease patients (PD) with MCI (PD-MCI), the effect of cognitive impairment on complex instrumental daily activities, such as medication management, is not well established., Method: 26 patients with PD-MCI (diagnosed to Level 2 Movement Disorders Society diagnostic criteria) and 32 idiopathic PD patients without cognitive impairment participated in the study. A detailed neuropsychological testing battery (including tests for attention and working memory, executive functions, language, visuospatial functions, episodic memory) and various prospective memory tasks were applied to the patients. Medication taking behaviors were evaluated using two different methods based on the performance (medication management ability assessment) and self-reporting (adherence scale)., Results: The PD-MCI group obtained significantly lower scores in medication management assessment and made more mistakes on following prescription instructions (e.g., they took more or less tablets and did not use medications as instructed with regard to meal times). Cognitive areas predicting success in medication management performance were language, event-based prospective memory and visuospatial functions. There was no significant difference between the two groups' self-reporting of adherence., Conclusion: Mild cognitive impairment in patients with PD adversely affects medication management. Diagnosing MCI in PD is important to ensure that the appropriate measures can be taken to provide support and improve the medication management process. Adherence assessments based on self-reporting may not provide reliable and sensitive information in patients with PD-MCI., (© 2022. The Author(s) under exclusive licence to Belgian Neurological Society.)

Diffusion MRI is a useful neuroimaging tool for non-invasive mapping of human brain microstructure and structural connections. The analysis of diffusion MRI data often requires brain segmentation, including volumetric segmentation and cerebral cortical surfaces, from additional high-resolution T 1 -weighted (T1w) anatomical MRI data, which may be unacquired, corrupted by subject motion or hardware failure, or cannot be accurately co-registered to the diffusion data that are not corrected for susceptibility-induced geometric distortion. To address these challenges, this study proposes to synthesize high-quality T1w anatomical images directly from diffusion data using convolutional neural networks (CNNs) (entitled "DeepAnat"), including a U-Net and a hybrid generative adversarial network (GAN), and perform brain segmentation on synthesized T1w images or assist the co-registration using synthesized T1w images. The quantitative and systematic evaluations using data of 60 young subjects provided by the Human Connectome Project (HCP) show that the synthesized T1w images and results for brain segmentation and comprehensive diffusion analysis tasks are highly similar to those from native T1w data. The brain segmentation accuracy is slightly higher for the U-Net than the GAN. The efficacy of DeepAnat is further validated on a larger dataset of 300 more elderly subjects provided by the UK Biobank. Moreover, the U-Nets trained and validated on the HCP and UK Biobank data are shown to be highly generalizable to the diffusion data from Massachusetts General Hospital Connectome Diffusion Microstructure Dataset (MGH CDMD) acquired with different hardware systems and imaging protocols and therefore can be used directly without retraining or with fine-tuning for further improved performance. Finally, it is quantitatively demonstrated that the alignment between native T1w images and diffusion images uncorrected for geometric distortion assisted by synthesized T1w images substantially improves upon that by directly co-registering the diffusion and T1w images using the data of 20 subjects from MGH CDMD. In summary, our study demonstrates the benefits and practical feasibility of DeepAnat for assisting various diffusion MRI data analyses and supports its use in neuroscientific applications., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) and retinal vasculopathy with cerebral leukodystrophy and systemic manifestations (RVCL-S) are the most common forms of rare monogenic early-onset cerebral small vessel disease and share clinical, and, to different extents, neuroradiological and neuropathological features. However, whether CADASIL and RVCL-S overlapping phenotype may be explained by shared genetic risk or causative factors such as TREX1 coding variants remains poorly understood. To investigate this intriguing hypothesis, we used exome sequencing to screen TREX1 protein-coding variability in a large multi-ethnic cohort of 180 early-onset independent familial and apparently sporadic CADASIL-like Caucasian patients from the USA, Portugal, Finland, Serbia and Turkey. We report 2 very rare and likely pathogenic TREX1 mutations: a loss of function mutation (p.Ala129fs) clustering in the catalytic domain, in an apparently sporadic 46-year-old patient from the USA and a missense mutation (p.Tyr305Cys) in the well conserved C-terminal region, in a 57-year-old patient with positive family history from Serbia. In concert with recent findings, our study expands the clinical spectrum of diseases associated with TREX1 mutations., Competing Interests: Disclosure statement All the authors declare no competing financial or personal interests that can influence the presented work. Written informed consent was obtained for each individual and the study was approved by the appropriate institutional review boards., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: One of the most common behavioral problems in patients with dementia is eating problems, which are known to increase the risk of malnutrition. However, few studies have been conducted in this patient group regarding the relationship between eating difficulties and nutritional status., Purpose: This study was designed to determine the eating difficulties faced by patients with dementia and to evaluate the relationship in this population between eating difficulties and malnutrition., Methods: This study was carried out in a dementia outpatient clinic of a university hospital in Istanbul, Turkey. This cross-sectional, case-control study included 50 patients and 50 healthy controls as participants. Participants were assessed for eating difficulties and for nutritional, cognitive, and functional statuses., Results: The patients with dementia had more difficulties in terms of self-feeding skills. Although problems related to manipulating food on the plate and the use of utensils were not seen in the control group, these problems were found in 30% of the participants in the dementia group ( p < .001). Moreover, 30% of the patients in the dementia group were unable to eat without assistance ( p < .001). Associations were found between eating difficulties and age, duration of illness, and cognitive and functional (basic and instrumental activities of daily living) statuses. In addition, self-feeding skills were found to be associated with nutritional status. Rate of malnutrition or risk of malnutrition was higher in patients with dementia than in those in the control group., Conclusions: In this study, compared with the control group, patients with dementia had more problems in self-feeding skills such as manipulation of food on a plate, use of utensils, need for assistive tools, ability to eat without assistance, and negative eating behaviors (refusal to eat). An association was found between eating difficulties and nutritional status. Evaluating eating difficulties is recommended in patients with dementia to prevent nutritional deterioration., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc.)

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm 2 . The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)