Magnetic Resonance Imaging Analysis Research Articles

Constrained Independent Vector Analysis with Reference for Multi-Subject fMRI Analysis.

Independent component analysis (ICA) is now a widely used solution for the analysis of multi-subject functional magnetic resonance imaging (fMRI) data. Independent vector analysis (IVA) generalizes ICA to multiple datasets (multi-subject data). Along with higher-order statistical information in ICA, it leverages the statistical dependence across the datasets as an additional type of statistical diversity. As such, IVA preserves variability in the estimation of single-subject maps but its performance might suffer when the number of datasets increases. Constrained IVA is an effective way to bypass computational issues and improve the quality of separation by incorporating available prior information. Existing constrained IVA approaches often rely on user-defined threshold values to define the constraints. However, an improperly selected threshold can have a negative impact on the final results. This paper proposes two novel methods for constrained IVA: one using an adaptive-reverse scheme to select variable thresholds for the constraints and a second one based on a threshold-free formulation by leveraging the unique structure of IVA. Notably, the proposed algorithms do not require all components to be constrained, utilizing free components to model interferences and components that might not be in the reference set. We demonstrate that our solutions provide an attractive solution to multi-subject fMRI analysis both by simulations and through analysis of resting state fMRI data collected from 98 subjects - the highest number of subjects ever used by IVA algorithms. Our results show that both proposed approaches obtain significantly better separation quality and model match while providing computationally efficient and highly reproducible solutions.

Application of a machine learning and optimization method to predict patellofemoral instability risk factors in children and adolescents.

Conservative treatment remains the standard approach for first-time patellar dislocations. While risk factors for patellofemoral instability, a common paediatric injury, are well-established in adults, data concerning the progression of paediatric recurrent patellar dislocation remain scarce. A reproducible method was developed to quantitatively assess the patellofemoral morphology and anatomic risk factors in paediatric patients using magnetic resonance imaging (MRI) and machine learning analysis. Data were analyzed from a retrospective review (2005-2022) of paediatric patients diagnosed with acute lateral patellar dislocation (54 patients) who underwent MRI and were compared with an age-based control group (54 patients). Patellofemoral, tibial, tibiofemoral and patellar height parameters were measured. Differences between groups were analyzed with respect to MRI parameters. The potential diagnostic utility of the parameters was assessed via machine learning and genetic algorithm analyses. Significant differences were observed between the two groups in six patellofemoral morphological parameters. Regarding patellar height morphological parameters, all methods exhibited significant between-group differences. Among the tibia and tibiofemoral morphological parameters, only the tibial tubercle-trochlear groove distance exhibited significant differences between the two groups. No sex-related differences were present. Significant variations were observed in patellar height parameters, particularly in the Koshino-Sugimoto (KS) index, which had the highest area under the curve (AUC: 0.87). Using genetic algorithms and logistic regression, our model excelled with seven key independent variables. KS index and Wiberg index had the strongest association with lateral patellar dislocation. An optimized logistic regression model achieved an AUC of 0.934. Such performance is considered clinically relevant, indicating the model's effectiveness for the intended application. Level Ⅲ.

Patellofemoral instability in children and adolescents

Patellofemoral instability is acommon and clinically relevant disorder of multifactorial causes. Several concomitant problems such as genua valga, hyperlaxity, injuries or sports-related overuse may contribute to the development of instability and recurrent patellar dislocations. Athorough diagnosis is of paramount importance to delineate every contributing factor. This includes radiographic modalities and advanced imaging such as magnetic resonance imaging or torsional analyses. The authors recommend non-operative management (including physiotherapy, gait and proprioceptive training, orthoses) and, whenever non-operative measures fail, surgical patellar stabilization using, e.g. MPFL reconstruction.

The Value of Magnetic Resonance Imaging in Assessing Immediate Efficacy After Microwave Ablation of Lung Malignancies.

To investigate the imaging performance and parametric analysis of magnetic resonance imaging (MRI) immediately after microwave ablation (MWA) of lung malignancies. We retrospectively analyzed the MRI performance immediately after MWA of 34 cases of lung malignancies. The ablation zone parameters of lung malignancies were measured, including the long diameter (L), short diameter (S), and safety margin of the ablation zone on plain computed tomography (CT), T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI) after MWA. The study calculated the tumor volume (V0), the ablation zone volume (V1), and the ratio of V0 to V1 (V%). Statistical differences between the parameters were analyzed. The ablation area of the lesion exhibited central low signal and peripheral high signal on T2WI, central high signal and peripheral equal or high signal on T1WI, and circumferential enhancement in the periphery. The safety margin measured on T2WI was greater than that measured on plain CT and T1WI. On plain CT, the L, S, and V1 were smaller in the effective treatment group than in the ineffective treatment group (P<0.05). On T1WI, the V% and safety margin were greater in the effective treatment group than in the ineffective treatment group (P=0.009 and P=0.016, respectively). MRI may be a new, valuable method to assess immediate efficacy after MWA for lung malignancies using the ablation zone parameters V% on T1WI and safety margin on T2WI.

Assessing cognitive impairment and disability in older adults through the lens of whole brain white matter patterns.

This study aimed to explore the potential of whole brain white matter patterns as novel neuroimaging biomarkers for assessing cognitive impairment and disability in older adults. We conducted an in-depth analysis of magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET) scans in 454 participants, focusing on white matter patterns and white matter inter-subject variability (WM-ISV). The white matter pattern ensemble model, combining MRI and amyloid PET, demonstrated a significantly higher classification performance for cognitive impairment and disability. Participants with Alzheimer's disease (AD) exhibited higher WM-ISV than participants with subjective cognitive decline, mild cognitive impairment, and vascular dementia. Furthermore, WM-ISV correlated significantly with blood-based biomarkers (such as glial fibrillary acidic protein and phosphorylated tau-217 [p-tau217]), and cognitive function and disability scores. Our results suggest that white matter pattern analysis has significant potential as an adjunct neuroimaging biomarker for clinical decision-making and determining cognitive impairment and disability. The ensemble model combined both magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET) and demonstrated a significantly higher classification performance for cognitive impairment and disability. Alzheimer's disease (AD) revealed a notably higher heterogeneity compared to that in subjective cognitive decline, mild cognitive impairment, or vascular dementia. White matter inter-subject variability (WM-ISV) was significantly correlated with blood-based biomarkers (glial fibrillary acidic protein and phosphorylated tau-217 [p-tau217]) and with the polygenic risk score for AD. White matter pattern analysis has significant potential as an adjunct neuroimaging biomarker for clinical decision-making processes and determining cognitive impairment and disability.

A comprehensive MRI analysis of osteoid osteomas in patients with diverse radiological features across various regions.

Magnetic resonance imaging (MRI), which does not involve ionizing radiation, is the preferred imaging modality for diagnosing osteoid osteoma (OO), an ailment more common in children and young adults. This study aims to perform a literature review and delineate the MRI findings of OO lesions in patients exhibiting varying radiological features across different regions. A retrospective study included 63 patients diagnosed with OO through MRI, assessed independently by two blinded radiologists using both standard and dynamic contrast-enhanced MRI techniques. After excluding 7 patients with prior biopsy, surgery, or RFA, the study included 56 patients with 57 lesions. Of 57 lesions evaluated, 50 were in long, and 7 in flat bones. One patient presented with two separate nidi within the intertrochanteric region. Most of the lesions, 49 (86%), were extra-articular, while 8 (14%) were intra-articular. The nidus was intracortical in 45 (78.9%) patients, intramedullary in 5 (8.8%), subperiosteal in 5 (8.8%), and endosteal in 2 (3.5%). Average nidus diameter was 7.02 ± 2.64 mm (3-12.6 mm). Central nidal calcification was present in 68.4% (n = 39) cases. Contrast enhancement was intense at 90.5%, moderate at 9.5%. Reactive sclerosis around the nidus was severe (50.9%), moderate (22.8%), and mild (26.3%). Bone marrow edema was severe (70.2%), moderate (14.0%), and mild (15.8%). Soft tissue edema was identified in 77.2% of all lesions. To minimize delays in diagnosis and treatment, radiologists should become acquainted with the typical OO MRI findings and the atypical MRI findings that might be mistaken for other conditions.

AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis.

TumorPrism3D software was developed to segment brain tumors with a straightforward and user-friendly graphical interface applied to two- and three-dimensional brain magnetic resonance (MR) images. The MR images of 185 patients (103 males, 82 females) with glioblastoma multiforme were downloaded from The Cancer Imaging Archive (TCIA) to test the tumor segmentation performance of this software. Regions of interest (ROIs) corresponding to contrast-enhancing lesions, necrotic portions, and non-enhancing T2 high signal intensity components were segmented for each tumor. TumorPrism3D demonstrated high accuracy in segmenting all three tumor components in cases of glioblastoma multiforme. They achieved a better Dice similarity coefficient (DSC) ranging from 0.83 to 0.91 than 3DSlicer with a DSC ranging from 0.80 to 0.84 for the accuracy of segmented tumors. Comparative analysis with the widely used 3DSlicer software revealed TumorPrism3D to be approximately 37.4% faster in the segmentation process from initial contour drawing to final segmentation mask determination. The semi-automated nature of TumorPrism3D facilitates reproducible tumor segmentation at a rapid pace, offering the potential for quantitative analysis of tumor characteristics and artificial intelligence-assisted segmentation in brain MR imaging.

AI driven analysis of MRI to measure health and disease progression in FSHD

Facioscapulohumeral muscular dystrophy (FSHD) affects roughly 1 in 7500 individuals. While at the population level there is a general pattern of affected muscles, there is substantial heterogeneity in muscle expression across- and within-patients. There can also be substantial variation in the pattern of fat and water signal intensity within a single muscle. While quantifying individual muscles across their full length using magnetic resonance imaging (MRI) represents the optimal approach to follow disease progression and evaluate therapeutic response, the ability to automate this process has been limited. The goal of this work was to develop and optimize an artificial intelligence-based image segmentation approach to comprehensively measure muscle volume, fat fraction, fat fraction distribution, and elevated short-tau inversion recovery signal in the musculature of patients with FSHD. Intra-rater, inter-rater, and scan-rescan analyses demonstrated that the developed methods are robust and precise. Representative cases and derived metrics of volume, cross-sectional area, and 3D pixel-maps demonstrate unique intramuscular patterns of disease. Future work focuses on leveraging these AI methods to include upper body output and aggregating individual muscle data across studies to determine best-fit models for characterizing progression and monitoring therapeutic modulation of MRI biomarkers.

Radiomics-based analysis of dynamic contrast-enhanced magnetic resonance image: A prediction nomogram for lymphovascular invasion in breast cancer

ObjectiveTo develop and validate a nomogram for quantitively predicting lymphovascular invasion (LVI) of breast cancer (BC) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and morphological features. MethodsWe retrospectively divided 238 patients with BC into training and validation cohorts. Radiomic features from DCE-MRI were subdivided into A1 and A2, representing the first and second post-contrast images respectively. We utilized the minimal redundancy maximal relevance filter to extract radiomic features, then we employed the least absolute shrinkage and selection operator regression to screen these features and calculate individualized radiomics score (Rad score). Through the application of multivariate logistic regression, we built a prediction nomogram that integrated DCE-MRI radiomics and MR morphological features (MR-MF). The diagnostic capabilities were evaluated by comparing C-indices and calibration curves. ResultsThe diagnostic efficiency of the A1/A2 radiomics model surpassed that of the A1 and A2 alone. Furthermore, we incorporated the MR-MF (diffusion-weighted imaging rim sign, peritumoral edema) and optimized Radiomics into a hybrid nomogram. The C-indices for the training and validation cohorts were 0.868 (95% CI: 0.839–0.898) and 0.847 (95% CI: 0.787–0.907), respectively, indicating a good level of discrimination. Moreover, the calibration plots demonstrated excellent agreement in the training and validation cohorts, confirming the effectiveness of the calibration. ConclusionThis nomogram combined MR-MF and A1/A2 Radiomics has the potential to preoperatively predict LVI in patients with BC.

Gait Analysis and Magnetic Resonance Imaging Characteristics in Patients with Isolated Rapid Eye Movement Sleep Behavior Disorder.

Isolated rapid eye movement sleep behavioral disorder (iRBD) can precede neurodegenerative diseases. There is an urgent need for biomarkers to aid early intervention and neuroprotection. The aim is to assess quantitative motor, cognitive, and brain magnetic resonance imaging (MRI) characteristics in iRBD patients. Thirty-eight polysomnography-confirmed iRBD patients and 28 age- and sex-matched healthy controls underwent clinical, cognitive, and motor functional evaluations, along with brain MRI. Motor tasks included nine-hole peg test, five-times-sit-to-stand test, timed-up-and-go test, and 4-meter walking test with and without cognitive dual task. Quantitative spatiotemporal gait parameters were obtained using an optoelectronic system. Brain MRI analysis included functional connectivity (FC) of the main resting-state networks, gray matter (GM) volume using voxel-based morphometry, cortical thickness, and deep GM and brainstem volumes using FMRIB's Integrated Registration and Segmentation Tool and FreeSurfer. iRBD patients relative to healthy subjects exhibited a poorer performance during the nine-hole peg test and five-times-sit-to-stand test, and greater asymmetry of arm-swing amplitude and stride length variability during dual-task gait. Dual task significantly worsened the walking performance of iRBD patients more than healthy controls. iRBD patients exhibited nonmotor symptoms, and memory, abstract reasoning, and visuospatial deficits. iRBD patients exhibited decreased FC of pallidum and putamen within the basal ganglia network and occipital and temporal areas within the visuo-associative network, and a reduced volume of the supramarginal gyrus. Brain functional alterations correlated with gait changes. Subtle motor and nonmotor alterations were identified in iRBD patients, alongside brain structural and functional MRI changes. These findings may represent early signs of neurodegeneration and contribute to the development of predictive models for progression to parkinsonism. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Radiomics and visual analysis for predicting success of transplantation of heterotopic glioblastoma in mice with MRI.

Quantifying tumor growth and treatment response noninvasively poses a challenge to all experimental tumor models. The aim of our study was, to assess the value of quantitative and visual examination and radiomic feature analysis of high-resolution MR images of heterotopic glioblastoma xenografts in mice to determine tumor cell proliferation (TCP). Human glioblastoma cells were injected subcutaneously into both flanks of immunodeficient mice and followed up on a 3T MR scanner. Volumes and signal intensities were calculated. Visual assessment of the internal tumor structure was based on a scoring system. Radiomic feature analysis was performed using MaZda software. The results were correlated with histopathology and immunochemistry. 21 tumors in 14 animals were analyzed. The volumes of xenografts with high TCP (H-TCP) increased, whereas those with low TCP (L-TCP) or no TCP (N-TCP) continued to decrease over time (p < 0.05). A low intensity rim (rim sign) on unenhanced T1-weighted images provided the highest diagnostic accuracy at visual analysis for assessing H-TCP (p < 0.05). Applying radiomic feature analysis, wavelet transform parameters were best for distinguishing between H-TCP and L-TCP / N-TCP (p < 0.05). Visual and radiomic feature analysis of the internal structure of heterotopically implanted glioblastomas provide reproducible and quantifiable results to predict the success of transplantation.

Measuring functional connectivity in frequency-domain helps to better characterize brain function.

Resting-state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence-based and correlation-based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging.

Characteristics of pain empathic networks in healthy and primary dysmenorrhea women: an fMRI study.

Pain empathy enables us to understand and share how others feel pain. Few studies have investigated pain empathy-related functional interactions at the whole-brain level across all networks. Additionally, women with primary dysmenorrhea (PDM) have abnormal pain empathy, and the association among the whole-brain functional network, pain, and pain empathy remain unclear. Using resting-state functional magnetic resonance imaging (fMRI) and machine learning analysis, we identified the brain functional network connectivity (FNC)-based features that are associated with pain empathy in two studies. Specifically, Study 1 examined 41 healthy controls (HCs), while Study 2 investigated 45 women with PDM. Additionally, in Study 3, a classification analysis was performed to examine the differences in FNC between HCs and women with PDM. Pain empathy was evaluated using a visual stimuli experiment, and trait and state of menstrual pain were recorded. In Study 1, the results showed that pain empathy in HCs relied on dynamic interactions across whole-brain networks and was not concentrated in a single or two brain networks, suggesting the dynamic cooperation of networks for pain empathy in HCs. In Study 2, PDM exhibited a distinctive network for pain empathy. The features associated with pain empathy were concentrated in the sensorimotor network (SMN). In Study 3, the SMN-related dynamic FNC could accurately distinguish women with PDM from HCs and exhibited a significant association with trait menstrual pain. This study may deepen our understanding of the neural mechanisms underpinning pain empathy and suggest that menstrual pain may affect pain empathy through maladaptive dynamic interaction between brain networks.

Radiomics and machine learning analysis of liver magnetic resonance imaging for prediction and early detection of tumor response in colorectal liver metastases.

The aim of this study was to demonstrate the effectiveness of a machine learning-based radiomics model for distinguishing tumor response and overall survival in patients with unresectable colorectal liver metastases (CRLM) treated with targeted biological therapy. We prospectively recruited 17 patients with unresectable liver metastases of colorectal cancer, who had been given targeted biological therapy as the first line of treatment. All patients underwent liver magnetic resonance imaging (MRI) three times up until 8 weeks after chemotherapy. We evaluated the diagnostic performance of machine learning-based radiomics model in tumor response of liver MRI compared with the guidelines for the Response Evaluation Criteria in Solid Tumors. Overall survival was evaluated using the Kaplan-Meier analysis and compared to the Cox proportional hazard ratios following univariate and multivariate analyses. Performance measurement of the trained model through metrics showed the accuracy of the machine learning model to be 76.5%, and the area under the receiver operating characteristic curve was 0.857 (95% confidence interval [CI], 0.605-0.976; P < 0.001). For the patients classified as non-progressing or progressing by the radiomics model, the median overall survival was 17.5 months (95% CI, 12.8-22.2), and 14.8 months (95% CI, 14.2-15.4), respectively (P = 0.431, log-rank test). Machine learning-based radiomics models could have the potential to predict tumor response in patients with unresectable CRLM treated with biologic therapy.

Value and potential pitfalls of morphometric analysis of magnetic resonance imaging in epilepsy.

Value and potential pitfalls of morphometric analysis of magnetic resonance imaging in epilepsy.

Impact of Ocrelizumab on Disease Progression, Memory Improvement, and Quality of Life in Patients with Relapsing-Remitting Multiple Sclerosis: A Longitudinal MRI and Clinical Criteria Analysis.

Multiple sclerosis (MS) is a chronic, progressive neurological disorder that significantly impacts quality of life and functionality. Ocrelizumab, a monoclonal antibody targeting CD20-positive B cells, has emerged as a treatment for relapsing-remitting MS (RRMS). This study aimed to assess the impact of ocrelizumab on disease progression and quality of life over a longitudinal course, utilizing clinical criteria and magnetic resonance imaging (MRI) analyses. Conducted at the Neurology Department of Pius Brinzeu Clinical Emergency Hospital in Western Romania from 2020 to 2023, this observational study enrolled 93 patients with RRMS who commenced ocrelizumab therapy. The study employed the Expanded Disability Status Scale (EDSS) and MRI to evaluate disease progression, while quality of life was assessed using the World Health Organisation Quality of Life (WHOQOL) questionnaire, Beck Depression Index (BDI), and MOCA scales. Significant improvements were observed post-treatment. EDSS scores decreased from 4.61 to 4.08 (p = 0.038), indicating reduced disability. MRI analyses showed a substantial decrease in expansive lesions (from 67.74% to 26.88%, p < 0.001) and an increase in stationary lesions (from 32.26% to 73.12%, p < 0.001). Quality of life improvements were notable in the physical (from 58.42 to 64.84, p = 0.005) and environmental domains (from 63.21 to 68.44, p = 0.033). Cognitive functions, assessed via Montreal Cognitive Assessment (MOCA), showed a significant total score increase from 20.38 to 22.30 (p < 0.001). Subgroup analysis revealed more pronounced effects in females and younger patients, with a significant reduction in depressive symptoms measured by BDI scores (from 14.35 to 11.62, p = 0.003). Ocrelizumab significantly reduced disease activity and disability in RRMS patients, as demonstrated by improvements in EDSS scores and MRI findings. Quality of life and cognitive functions also showed considerable enhancements. These findings support ocrelizumab's efficacy in not only managing MS symptoms but also improving overall patient well-being.

Stereotactically intracerebral transplantation of neural stem cells for ischemic stroke attenuated inflammatory responses and promoted neurogenesis: an experimental study with monkeys.

Ischemic stroke is a common neurovascular disorder with high morbidity and mortality. However, the underlying mechanism of stereotactically intracerebral transplantation of human neural stem cell (hNSC) is not well elucidated. Four days after ischemic stroke induced by Rose Bengal photo-thrombosis, 7 cynomolgus monkeys were transplanted with hNSCs or vehicles stereotactically and followed up for 84 days. Behavioral assessments, magnetic resonance imaging, blood tests, and pathological analysis were performed before and after treatment. The proteome profiles of the left and right precentral gyrus and hippocampus were evaluated. Extracellular vesicle micro-RNA (miRNA) from the peripheral blood was extracted and analyzed. hNSC transplantation reduced the remaining infarcted lesion volume of cynomolgus monkeys with ischemic stroke without remarkable side effects. Proteomic analyses indicated that hNSC transplantation promoted GABAergic and glutamatergic neurogenesis, and restored the mitochondrial electron transport chain function in the ischemic infarcted left precentral gyrus or hippocampus. Immunohistochemical staining and qRT-PCT confirmed the promoting effects on neurogenesis and revealed that hNSCs attenuated post-infarct inflammatory responses by suppressing resident glia activation and mediating peripheral immune cell infiltration. Consistently, miRNA-sequencing revealed the miRNAs which were related to these pathways were down-regulated after hNSC transplantation. This study indicates that hNSCs can be effectively and safely used to treat ischemic stroke by promoting neurogenesis, regulating post-infarct inflammatory responses, and restoring mitochondrial function in both the infarct region and hippocampus.

Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach.

The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error. This research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans. The dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification. The proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications. This study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods.

Parallel Multilink Group Joint ICA: Fusion of 3D Structural and 4D Functional Data Across Multiple Resting fMRI Networks.

Multimodal neuroimaging research plays a pivotal role in understanding the complexities of the human brain and its disorders. Independent component analysis (ICA) has emerged as a widely used and powerful tool for disentangling mixed independent sources, particularly in the analysis of functional magnetic resonance imaging (fMRI) data. This paper extends the use of ICA as a unifying framework for multimodal fusion, introducing a novel approach termed parallel multilink group joint ICA (pmg-jICA). The method allows for the fusion of gray matter maps from structural MRI (sMRI) data to multiple fMRI intrinsic networks, addressing the limitations of previous models. The effectiveness of pmg-jICA is demonstrated through its application to an Alzheimer's dataset, yielding linked structure-function outputs for 53 brain networks. Our approach leverages the complementary information from various imaging modalities, providing a unique perspective on brain alterations in Alzheimer's disease. The pmg-jICA identifies several components with significant differences between HC and AD groups including thalamus, caudate, putamen with in the subcortical (SC) domain, insula, parahippocampal gyrus within the cognitive control (CC) domain, and the lingual gyrus within the visual (VS) domain, providing localized insights into the links between AD and specific brain regions. In addition, because we link across multiple brain networks, we can also compute functional network connectivity (FNC) from spatial maps and subject loadings, providing a detailed exploration of the relationships between different brain regions and allowing us to visualize spatial patterns and loading parameters in sMRI along with intrinsic networks and FNC from the fMRI data. In essence, developed approach combines concepts from joint ICA and group ICA to provide a rich set of output characterizing data-driven links between covarying gray matter networks, and a (potentially large number of) resting fMRI networks allowing further study in the context of structure/function links. We demonstrate the utility of the approach by highlighting key structure/function disruptions in Alzheimer's individuals.

Reduced olfactory bulb volume accompanies olfactory dysfunction after mild SARS-CoV-2 infection

Despite its high prevalence, the determinants of smelling impairment in COVID-19 remain not fully understood. In this work, we aimed to examine the association between olfactory bulb volume and the clinical trajectory of COVID-19-related smelling impairment in a large-scale magnetic resonance imaging (MRI) analysis. Data of non-vaccinated COVID-19 convalescents recruited within the framework of the prospective Hamburg City Health Study COVID Program between March and December 2020 were analyzed. At baseline, 233 participants underwent MRI and neuropsychological testing as well as a structured questionnaire for olfactory function. Between March and April 2022, olfactory function was assessed at follow-up including quantitative olfactometric testing with Sniffin’ Sticks. This study included 233 individuals recovered from mainly mild to moderate SARS-CoV-2 infections. Longitudinal assessment demonstrated a declining prevalence of self-reported olfactory dysfunction from 67.1% at acute infection, 21.0% at baseline examination and 17.5% at follow-up. Participants with post-acute self-reported olfactory dysfunction had a significantly lower olfactory bulb volume at baseline than normally smelling individuals. Olfactory bulb volume at baseline predicted olfactometric scores at follow-up. Performance in neuropsychological testing was not significantly associated with the olfactory bulb volume. Our work demonstrates an association of long-term self-reported smelling dysfunction and olfactory bulb integrity in a sample of individuals recovered from mainly mild to moderate COVID-19. Collectively, our results highlight olfactory bulb volume as a surrogate marker that may inform diagnosis and guide rehabilitation strategies in COVID-19.