Magnetic Resonance Imaging Data Research Articles

129 Neuroimaging Analysis of Bilateral Thalamic Delivery for Gene Therapy

INTRODUCTION: Axonal transport from the thalamus (an integrative hub for the entire cortex) has been shown to facilitate widespread gene/protein delivery. Here, we report our neuroimaging experience with bilateral thalamic convection-enhanced delivery of adeno-associated virus (AAV) vectors for GM2 gangliosidoses. METHODS: Magnetic resonance imaging (MRI) of eight pediatric patients who underwent bilateral thalamic HEXA/HEXB delivery were retrospectively reviewed. Quantitative three-dimensional volumetric analyses of MRI thalamic signals were used to establish volume of distribution (Vd) versus volume of infusion (Vi). The distance of cannula placement within the thalamus was calculated and validated using the numbers used in the intraoperative ROSA robotic software. Backflow and leakage into surrounding structures were also analyzed. RESULTS: Fourteen thalamic injection volumes were considered in the final analysis. Calculated volume of thalamic infusate signal was up to 43.8% (mean = 21.3±13.3) of the thalamic volume. The Vd/Vi ratio ranged from 0.4-2.7 (mean = 1.9±0.7), depending on infusate volume. Diffusate shape was round to slightly ellipsoid in all patients. Backflow and leakage were encountered in 14.3% and 12.5%, respectively, of thalamic injections. CONCLUSIONS: This study provides the first in-human quantitative MRI data for intrathalamic infusion. The ideal location for the cannula tip was determined to be between the mediodorsal and lateral dorsal nuclei and the ideal trajectory keeps all of the cannula steps within the thalamic boundaries. We also compare these data to published NHP and our own NHP and sheep data. Overall, the Vd/Vi ratios are lower in human subjects (∼2) than in animal subjects (∼3). We hypothesize that this may be due in part to species specific differences in displacement of extracellular fluid versus infusion volume.

Autism-associated brain differences can be observed in utero using MRI.

Developmental changes that occur before birth are thought to be associated with the development of autism spectrum disorders. Identifying anatomical predictors of early brain development may contribute to our understanding of the neurobiology of autism spectrum disorders and allow for earlier and more effective identification and treatment of autism spectrum disorders. In this study, we used retrospective clinical brain magnetic resonance imaging data from fetuses who were diagnosed with autism spectrum disorders later in life (prospective autism spectrum disorders) in order to identify the earliest magnetic resonance imaging-based regional volumetric biomarkers. Our results showed that magnetic resonance imaging-based autism spectrum disorder biomarkers can be found as early as in the fetal period and suggested that the increased volume of the insular cortex may be the most promising magnetic resonance imaging-based fetal biomarker for the future emergence of autism spectrum disorders, along with some additional, potentially useful changes in regional volumes and hemispheric asymmetries.

The value of the dermal rim sign on nonenhanced magnetic resonance imaging for predicting dermal backflow in patients with primary lower extremity lymphedema

PurposeThe dermal rim sign (DRS) on nonenhanced magnetic resonance imaging has been shown to predict dermal backflow (DBF) in patients with secondary upper limb lymphedema. However, whether the DRS has the same effects on primary lower extremity lymphedema (PLEL) has not been clearly reported. Therefore, this study aimed to explore whether the DRS can be used to diagnose DBF on lymphoscintigraphy in patients with PLEL. MethodsA total of 94 patients who were diagnosed with PLEL were recruited for this retrospective study from January 2022 to December 2023. All the patients were divided into two groups according to the lymphoscintigraphy findings: no DBF and DBF. The magnetic resonance imaging data of the two groups were recorded and statistically compared for the following indicators: range of lymphedema involvement (left, right, whole lower limbs, only thigh, only calf and ankle), signs of lymphedema (notable thickening of skin, parallel line sign, grid sign, honeycomb sign, band sign, lymph lake sign, crescent sign, DRS), and lymphedema measurement (skin thickness, band width). The DRS is characterized by notable thickening of the skin plus the grid sign and/or honeycomb sign, plus the band sign. ResultsThe following statistically significant differences in the following indicators were found between the two groups (P < .05): notable skin thickening, parallel line sign, grid sign, honeycomb sign, band sign, DRS, skin thickness, and band width. The sensitivity, specificity, and accuracy for predicting for DBF with the DRS was 82%, 64%, and 77%, respectively. ConclusionsThis study confirmed good consistency between the DRS and DBF from the perspective of imaging. This tool is suitable for children, adolescents, and patients with contraindications to lymphoscintigraphy. The DRS has important value in assessing the severity of PLEL. The DRS is suggested for the clinical use of combined surgical treatment of PLEL.

Intrinsic network abnormalities in children with autism spectrum disorder: an independent component analysis.

Aberrant intrinsic brain networks are consistently observed in individuals with autism spectrum disorder. However, studies examining the strength of functional connectivity across brain regions have yielded conflicting results. Therefore, this study aimed to investigate the functional connectivity of the resting brain in children with low-functioning autism, including during the early developmental stages. We explored the functional connectivity of 43 children with autism spectrum disorder and 54 children with typical development aged 2 to 12 years using resting-state functional magnetic resonance imaging data. We used independent component analysis to classify the brain regions into six intrinsic networks and analyzed the functional connectivity within each network. Moreover, we analyzed the relationship between functional connectivity and clinical scores. In children with autism, the under-connectivities were observed within several brain networks, including the cognitive control, default mode, visual, and somatomotor networks. In contrast, we found over-connectivities between the subcortical, visual, and somatomotor networks in children with autism compared with children with typical development. Moderate effect sizes were observed in entire networks (Cohen's d = 0.43-0.77). These network alterations were significantly correlated with clinical scores such as the communication sub-score (r = - 0.442, p = 0.045) and the calibrated severity score (r = - 0.435, p = 0.049) of the Autism Diagnostic Observation Schedule. These opposing results observed based on the brain areas suggest that aberrant neurodevelopment proceeds in various ways depending on the functional brain regions in individuals with autism spectrum disorder.

Contributions of Cerebral White Matter Hyperintensities to Postural Instability in Aging With and Without Alcohol Use Disorder

BackgroundBoth postural instability and brain white matter hyperintensities (WMHs) are noted markers of normal aging and alcohol use disorder (AUD). Here, we questioned what variables contribute to the sway path–WMH relationship in individuals with AUD and healthy control participants. MethodsThe data comprised 404 balance platform sessions, yielding sway path length and magnetic resonance imaging data acquired cross-sectionally or longitudinally in 102 control participants and 158 participants with AUD ages 25 to 80 years. Balance sessions were typically conducted on the same day as magnetic resonance imaging fluid-attenuated inversion recovery acquisitions, permitting WMH volume quantification. Factors considered in multiple regression analyses as potential contributors to the relationship between WMH volumes and postural instability were age, sex, socioeconomic status, education, pedal 2-point discrimination, systolic and diastolic blood pressure, body mass index, depressive symptoms, total alcohol consumed in the past year, and race. ResultsInitial analysis identified diagnosis, age, sex, and race as significant contributors to observed sway path–WMH relationship. Inclusion of these factors as predictors in multiple regression analyses substantially attenuated the sway path–WMH relationship in both AUD and healthy control groups. Women, irrespective of diagnosis or race, had shorter sway paths than men. Black participants, irrespective of diagnosis or sex, had shorter sway paths than non-Black participants despite having modestly larger WMH volumes than non-Black participants, which is possibly a reflection of the younger age of the Black sample. ConclusionsLonger sway paths were related to larger WMH volumes in healthy men and women with and without AUD. Critically, however, age almost fully accounted for these associations.

Modeling refined differences of cortical folding patterns via spatial, morphological, and temporal fusion representations.

The gyrus, a pivotal cortical folding pattern, is essential for integrating brain structure-function. This study focuses on 2-Hinge and 3-Hinge folds, characterized by the gyral convergence from various directions. Existing voxel-level studies may not adequately capture the precise spatial relationships within cortical folding patterns, especially when relying solely on local cortical characteristics due to their variable shapes and homogeneous frequency-specific features. To overcome these challenges, we introduced a novel model that combines spatial distribution, morphological structure, and functional magnetic resonance imaging data. We utilized spatio-morphological residual representations to enhance and extract subtle variations in cortical spatial distribution and morphological structure during blood oxygenation, integrating these with functional magnetic resonance imaging embeddings using self-attention for spatio-morphological-temporal representations. Testing these representations for identifying cortical folding patterns, including sulci, gyri, 2-Hinge, and 2-Hinge folds, and evaluating the impact of phenotypic data (e.g. stimulus) on recognition, our experimental results demonstrate the model's superior performance, revealing significant differences in cortical folding patterns under various stimulus. These differences are also evident in the characteristics of sulci and gyri folds between genders, with 3-Hinge showing more variations. Our findings indicate that our representations of cortical folding patterns could serve as biomarkers for understanding brain structure-function correlations.

Training in Cognitive Reappraisal Normalizes Whole-Brain Indices of Emotion Regulation in Borderline Personality Disorder

BackgroundBorderline personality disorder (BPD) is the prototypical disorder of emotion dysregulation. We have previously shown that patients with BPD are impaired in their capacity to engage cognitive reappraisal, a frequently employed adaptive emotion regulation strategy. MethodsHere, we report on the efficacy of longitudinal training in cognitive reappraisal to enhance emotion regulation in patients with BPD. Specifically, the training targeted psychological distancing, a reappraisal tactic whereby negative stimuli are viewed dispassionately as though experienced by an objective, impartial observer. At each of 5 sessions over 2 weeks, 22 participants with BPD (14 female) and 22 healthy control participants (13 female) received training in psychological distancing and then completed a widely used picture-based reappraisal task. Self-reported negative affect ratings and functional magnetic resonance imaging data were acquired at the first and fifth sessions. In addition to behavioral analyses, we performed whole-brain pattern expression analyses using independently defined patterns for negative affect and cognitive reappraisal implementation for each session. ResultsPatients with BPD showed a decrease in negative affect pattern expression following reappraisal training, reflecting a normalization in neural activity. However, they did not show significant change in behavioral self-reports. ConclusionsTo our knowledge, this study represents the first longitudinal functional magnetic resonance imaging examination of task-based cognitive reappraisal training. Using a brief, proof-of-concept design, the results suggest a potential role for reappraisal training in the treatment of patients with BPD.

Magnetic Resonance Imaging, Clinical, and Biopsy Findings in Suspected Prostate Cancer

Multiple strategies integrating magnetic resonance imaging (MRI) and clinical data have been proposed to determine the need for a prostate biopsy in men with suspected clinically significant prostate cancer (csPCa) (Gleason score ≥3 + 4). However, inconsistencies across different strategies create challenges for drawing a definitive conclusion. To determine the optimal prostate biopsy decision-making strategy for avoiding unnecessary biopsies and minimizing the risk of missing csPCa by combining MRI Prostate Imaging Reporting & Data System (PI-RADS) and clinical data. PubMed, Ovid MEDLINE, Embase, Web of Science, and Cochrane Library from inception to July 1, 2022. English-language studies that evaluated men with suspected but not confirmed csPCa who underwent MRI PI-RADS followed by prostate biopsy were included. Each study had proposed a biopsy plan by combining PI-RADS and clinical data. Studies were independently assessed for eligibility for inclusion. Quality of studies was appraised using the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the Newcastle-Ottawa Scale. Mixed-effects meta-analyses and meta-regression models with multimodel inference were performed. Reporting of this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Independent risk factors of csPCa were determined by performing meta-regression between the rate of csPCa and PI-RADS and clinical parameters. Yields of different biopsy strategies were assessed by performing diagnostic meta-analysis. The analyses included 72 studies comprising 36 366 patients. Univariable meta-regression showed that PI-RADS 4 (β-coefficient [SE], 7.82 [3.85]; P = .045) and PI-RADS 5 (β-coefficient [SE], 23.18 [4.46]; P < .001) lesions, but not PI-RADS 3 lesions (β-coefficient [SE], -4.08 [3.06]; P = .19), were significantly associated with a higher risk of csPCa. When considered jointly in a multivariable model, prostate-specific antigen density (PSAD) was the only clinical variable significantly associated with csPCa (β-coefficient [SE], 15.50 [5.14]; P < .001) besides PI-RADS 5 (β-coefficient [SE], 9.19 [3.33]; P < .001). Avoiding biopsy in patients with lesions with PI-RADS category of 3 or less and PSAD less than 0.10 (vs <0.15) ng/mL2 resulted in reducing 30% (vs 48%) of unnecessary biopsies (compared with performing biopsy in all suspected patients), with an estimated sensitivity of 97% (vs 95%) and number needed to harm of 17 (vs 15). These findings suggest that in patients with suspected csPCa, patient-tailored prostate biopsy decisions based on PI-RADS and PSAD could prevent unnecessary procedures while maintaining high sensitivity.

Down-sampling in diffusion MRI: a bundle-specific DTI and NODDI study.

Multi-shell diffusion Magnetic Resonance Imaging (dMRI) data has been widely used to characterise white matter microstructure in several neurodegenerative diseases. The lack of standardised dMRI protocols often implies the acquisition of redundant measurements, resulting in prolonged acquisition times. In this study, we investigate the impact of the number of gradient directions on Diffusion Tensor Imaging (DTI) and on Neurite Orientation Dispersion and Density Imaging (NODDI) metrics. Data from 124 healthy controls collected in three different longitudinal studies were included. Using an in-house algorithm, we reduced the number of gradient directions in each data shell. We estimated DTI and NODDI measures on six white matter bundles clinically relevant for neurodegenerative diseases. Fractional Anisotropy (FA) measures on bundles where data were sampled at the 30% rate, showed a median L1 distance of up to 3.92% and a 95% CI of (1.74, 8.97)% when compared to those obtained at reference sampling. Mean Diffusivity (MD) reached up to 4.31% and a 95% CI of (1.60, 16.98)% on the same premises. At a sampling rate of 50%, we obtained a median of 3.90% and a 95% CI of (1.99, 16.65)% in FA, and 5.49% with a 95% CI of (2.14, 21.68)% in MD. The Intra-Cellular volume fraction (ICvf) median L1 distance was up to 2.83% with a 95% CI of (1.98, 4.82)% at a 30% sampling rate and 3.95% with a 95% CI of (2.39, 7.81)% at a 50% sampling rate. The volume difference of the reconstructed white matter at reference and 50% sampling reached a maximum of (2.09 ± 0.81)%. In conclusion, DTI and NODDI measures reported at reference sampling were comparable to those obtained when the number of dMRI volumes was reduced by up to 30%. Close to reference DTI and NODDI metrics were estimated with a significant reduction in acquisition time using three shells, respectively with: 4 directions at a b value of 700 s/mm2, 14 at 1000 s/mm2, and 32 at 2000 s/mm2. The study revealed aspects that can be important for large-scale clinical studies on bundle-specific diffusion MRI.

Comparative MRI analysis of the forebrain of three sauropsida models.

The study of the brain by magnetic resonance imaging (MRI) allows to obtain detailed anatomical images, useful to describe specific encephalic structures and to analyze possible variabilities. It is widely used in clinical practice and is becoming increasingly used in veterinary medicine, even in exotic animals; however, despite its potential, its use in comparative neuroanatomy studies is still incipient. It is a technology that in recent years has significantly improved anatomical resolution, together with the fact that it is non-invasive and allows for systematic comparative analysis. All this makes it particularly interesting and useful in evolutionary neuroscience studies, since it allows for the analysis and comparison of brains of rare or otherwise inaccessible species. In the present study, we have analyzed the prosencephalon of three representative sauropsid species, the turtle Trachemys scripta (order Testudine), the lizard Pogona vitticeps (order Squamata) and the snake Python regius (order Squamata) by MRI. In addition, we used MRI sections to analyze the total brain volume and ventricular system of these species, employing volumetric and chemometric analyses together. The raw MRI data of the sauropsida models analyzed in the present study are available for viewing and downloading and have allowed us to produce an atlas of the forebrain of each of the species analyzed, with the main brain regions. In addition, our volumetric data showed that the three groups presented clear differences in terms of total and ventricular brain volumes, particularly the turtles, which in all cases presented distinctive characteristics compared to the lizards and snakes.

Brain magnetic resonance imaging review suggests unrecognised hypoglycaemia in childhood.

Neonatal and early-life hypoglycaemia, is a frequent finding but is often non-specific and asymptomatic, making detection and diagnosis challenging. Hypoglycaemia-induced cerebral injury can be identified by magnetic resonance imaging (MRI) changes in cerebral white matter, occipital lobes, and posterior parietotemporal regions. It is unknown if children may have hypoglycaemic brain injury secondary to unrecognised hypoglycaemia in early life. We have examined retrospective radiological findings of likely brain injury by neuroimaging to investigate the existence of previous missed hypoglycaemic events. Retrospective MRI data in children in a single tertiary centre, over a ten-year period was reviewed to identify potential cases of unrecognised early-life hypoglycaemia. A detailed search from an electronic radiology repository involved the term "hypoglycaemia'' from text-based reports. The initial report was used for those who required serial scanning. Images specific to relevant reports were further reviewed by a designated paediatric neuroradiologist to confirm likely hypoglycaemia induced brain injury. Medical records of those children were subsequently reviewed to assess if the hypoglycaemia had been diagnosed prior to imaging. A total of 107 MR imaging reports were identified for review, and 52 (48.5%) showed typical features strongly suggestive of hypoglycaemic brain injury. Medical note review confirmed no documented clinical information of hypoglycaemia prior to imaging in 22 (42%) patients, raising the likelihood of missed hypoglycaemic events resulting in brain injury. We have identified the existence of unrecognised childhood hypoglycaemia through neuroimaging review. This study highlights the need for heightened awareness of early life hypoglycaemia to prevent adverse neurological outcomes later in childhood.

Machine Learning Approaches to Identify Affected Brain Regions in Movement Disorders Using MRI Data: A Systematic Review and Diagnostic Meta-analysis.

Movement disorders such as Parkinson's disease are associated with structural and functional changes in specific brain regions. Advanced magnetic resonance imaging (MRI) techniques combined with machine learning (ML) are promising tools for identifying imaging biomarkers and patterns associated with these disorders. We aimed to systematically identify the brain regions most commonly affected in movement disorders using ML approaches applied to structural and functional MRI data. We searched the PubMed and Scopus databases using relevant keywords up to June 2023 for studies that used ML approaches to detect brain regions associated with movement disorders using MRI data. A systematic review and diagnostic meta-analysis. Sixty-seven studies with 6,285 patients were included. Studies utilizing 1.5T or 3T MR scanners and the acquisition of diffusion tensor imaging (DTI), structural MRI (sMRI), functional MRI (fMRI), or a combination of these were included. The authors independently assessed the study quality using the CLAIM and QUADAS-2 criteria and extracted data on diagnostic accuracy measures. Sensitivity, specificity, accuracy, and area under the curve were pooled using random-effects models. Q statistics and the I2 index were used to evaluate heterogeneity, and Begg's funnel plot was used to identify publication bias. sMRI showed the highest sensitivity (93%) and mixed modalities had the highest specificity (90%) for detecting regional abnormalities. sMRI had a 94% sensitivity for identifying subcortical changes. The support vector machine (93%) and logistic regression (91%) models exhibited high diagnostic accuracies. The combination of advanced MR neuroimaging techniques and ML is a promising approach for identifying brain biomarkers and affected regions in movement disorders with subcortical structures frequently implicated. Structural MRI, in particular, showed strong performance. 1 TECHNICAL EFFICACY: Stage 2.

Abnormalities of white matter network properties in middle-aged and elderly patients with functional constipation.

To explore white matter network topological properties changes in middle-aged and elderly patients with functional constipation (Functional Constipation, FC) by diffusion tensor imaging (DTI), and to evaluate the correlation between the abnormal changes and clinical data. 29 FC patients and 31 age- and sex-matched healthy controls (HC) were recruited. Magnetic resonance imaging and clinical data were collected. The white matter network changes in FC patients were analyzed using deterministic fiber tracking methods, graph theory algorithms, and partial correlation analysis with clinical data. The nodal clustering coefficient and nodal local efficiency of FC patients in the right orbital inferior frontal gyrus, right medial superior frontal gyrus, right rectus muscle, right hippocampus, left paracentral lobule and left temporal pole, and the nodal clustering coefficient in right orbital superior frontal gyrus, left cuneus lobe and right superior occipital gyrus, the nodal local efficiency in the right medial and paracingulate gyrus, right precuneus and right dorsolateral superior frontal gyrus of FC patients are lower than that of HC. The nodal local efficiency and clustering coefficient of FC patients in left hippocampus, left amygdala, right parietal inferior limbic angular gyrus and right angular gyrus, the nodal local efficiency in the right fusiform gyrus, left supplementary motor cortex and the nodal efficiency in the left lateral temporal gyrus and right orbital middle frontal gyrus (ORBmid.R) of FC patients are higher than that of HC. The nodal efficiency of ORBmid.R in FC was positively correlated with the Patient Assessment of Constipation quality of life questionnaire (PAC-QoL). Middle-aged and elderly FC patients have differences in the nodal level properties in the limbic system, supplementary motor cortex, and default mode network brain regions, and the nodal efficiency of ORBmid.R was positively correlated with the PAC-QoL score, revealing that FC may be related to the abnormal processing of visceral sensorimotor in ORBmid.R and providing potential imaging diagnostic markers and therapeutic targets for middle-aged and elderly FC patients.

Resting-state effective connectivity is systematically linked to reappraisal success of high- and low-intensity negative emotions.

Emotion regulation is a process by which individuals modulate their emotional responses to cope with different environmental demands, for example, by reappraising the emotional situation. Here, we tested whether effective connectivity of a reappraisal-related neural network at rest is predictive of successfully regulating high- and low-intensity negative emotions in an emotion-regulation task. Task-based and resting-state functional magnetic resonance imaging (rs-fMRI) data of 28 participants were collected using ultra-high magnetic field strength at 7 Tesla during three scanning sessions. We used spectral dynamic causal modeling (spDCM) on the rs-fMRI data within brain regions modulated by emotion intensity. We found common connectivity patterns for both high- and low-intensity stimuli. Distinctive effective connectivity patterns in relation to low-intensity stimuli were found from frontal regions connecting to temporal regions. Reappraisal success for high-intensity stimuli was predicted by additional connections within the vlPFC and from temporal to frontal regions. Connectivity patterns at rest predicting reappraisal success were generally more pronounced for low-intensity stimuli, suggesting a greater role of stereotyped patterns, potentially reflecting preparedness, when reappraisal was relatively easy to implement. The opposite was true for high-intensity stimuli, which might require a more flexible recruitment of resources beyond what is reflected in resting state connectivity patterns. Resting-state effective connectivity emerged as a robust predictor for successful reappraisal, revealing both shared and distinct network dynamics for high- and low-intensity stimuli. These patterns signify specific preparatory states associated with heightened vigilance, attention, self-awareness, and goal-directed cognitive processing, particularly during reappraisal for mitigating the emotional impact of external stimuli. Our findings hold potential implications for understanding psychopathological alterations in brain connectivity related to affective disorders.

Identification of Severe Grading in Knee OsteoArthritis from MRI using Ensemble Deep Learning.

Accurate finding of Knee Osteoarthritis (KOA) from structural Magnetic Resonance Imaging (MRI) is a difficult task and is greatly subject to user variation. Furthermore, the identification of knee osteoarthritis (KOA) from MRI scans presents a challenge due to the limited information available. A novel methodology using an ensemble Deep Learning algorithm, combining EfficientNet-B3 and ResNext-101 architectures, aims to forecast KOA advancement, bridging the identified gap in clinical trials. The study aims to develop a precise predictive model for knee osteoarthritis using advanced deep-learning architectures and structural MRI scan data. By utilizing an ensemble technique, the model's accuracy in predicting disease development is enhanced, surpassing the limitations of traditional biomarkers. The study used the Osteoarthritis Initiative dataset to develop an ensemble Deep Learning model that combined EfficientNet-B3 and ResNext-101 architectures. Techniques like cropping, gamma correction, and in-slice rotation were used to expand the dataset and improve the model's generalization capacity. The Deep Learning model demonstrated 93% validation accuracy on the OAI dataset, accurately capturing subtle patterns of knee osteoarthritis progression. Augmentation approaches enhanced its resilience. Our ensemble Deep Learning approach, using ResNext-101 and EfficientNet-B3 architectures, accurately predicts knee osteoarthritis courses using structural MRI data, demonstrating the importance of data augmentation for improved predictive tools.

Exploring Deep Learning Models for Accurate Alzheimer's Disease Classification based on MRI Imaging

INTRODUCTION: Alzheimer's disease (AD), a complex neurodegenerative condition, presents significant challenges in early and accurate diagnosis. Early prediction of AD severity holds the potential for improved patient care and timely interventions. This research investigates the use of deep learning methodologies to forecast AD severity utilizing data extracted from Magnetic Resonance Imaging (MRI) scans.&#x0D; OBJECTIVES: This study aims to explore the efficacy of deep learning models in predicting the severity of Alzheimer's disease using MRI data. Traditional diagnostic methods for AD, primarily reliant on cognitive assessments, often lead to late-stage detection. MRI scans offer a non-invasive means to examine brain structure and detect pathological changes associated with AD. However, manual interpretation of these scans is labor-intensive and subject to variability.&#x0D; METHODS: Various deep learning models, including Convolutional Neural Networks (CNNs) and advanced architectures like DenseNet, VGG16, ResNet50, MobileNet, AlexNet, and Xception, are explored for MRI scan analysis. The performance of these models in predicting AD severity is assessed and compared. Deep learning models autonomously learn hierarchical features from the data, potentially recognizing intricate patterns associated with different AD stages that may be overlooked in manual analysis.&#x0D; RESULTS: The study evaluates the performance of different deep learning models in predicting AD severity using MRI scans. The results highlight the efficacy of these models in capturing subtle patterns indicative of AD progression. Moreover, the comparison underscores the strengths and limitations of each model, aiding in the selection of appropriate methodologies for AD prognosis.&#x0D; CONCLUSION: This research contributes to the growing field of AI-driven healthcare by showcasing the potential of deep learning in revolutionizing AD diagnosis and prognosis. The findings emphasize the importance of leveraging advanced technologies, such as deep learning, to enhance the accuracy and timeliness of AD diagnosis. However, challenges remain, including the need for large, annotated datasets, model interpretability, and integration into clinical workflows. Continued efforts in this area hold promise for improving the management of AD and ultimately enhancing patient outcomes.

SuMRak: a multi-tool solution for preclinical brain MRI data analysis.

Magnetic resonance imaging (MRI) is invaluable for understanding brain disorders, but data complexity poses a challenge in experimental research. In this study, we introduce suMRak, a MATLAB application designed for efficient preclinical brain MRI analysis. SuMRak integrates brain segmentation, volumetry, image registration, and parameter map generation into a unified interface, thereby reducing the number of separate tools that researchers may require for straightforward data handling. All functionalities of suMRak are implemented using the MATLAB App Designer and the MATLAB-integrated Python engine. A total of six helper applications were developed alongside the main suMRak interface to allow for a cohesive and streamlined workflow. The brain segmentation strategy was validated by comparing suMRak against manual segmentation and ITK-SNAP, a popular open-source application for biomedical image segmentation. When compared with the manual segmentation of coronal mouse brain slices, suMRak achieved a high Sørensen-Dice similarity coefficient (0.98 ± 0.01), approaching manual accuracy. Additionally, suMRak exhibited significant improvement (p = 0.03) when compared to ITK-SNAP, particularly for caudally located brain slices. Furthermore, suMRak was capable of effectively analyzing preclinical MRI data obtained in our own studies. Most notably, the results of brain perfusion map registration to T2-weighted images were shown, improving the topographic connection to anatomical areas and enabling further data analysis to better account for the inherent spatial distortions of echoplanar imaging. SuMRak offers efficient MRI data processing of preclinical brain images, enabling researchers' consistency and precision. Notably, the accelerated brain segmentation, achieved through K-means clustering and morphological operations, significantly reduces processing time and allows for easier handling of larger datasets.

Menarche, pubertal timing and the brain: female-specific patterns of brain maturation beyond age-related development

BackgroundPuberty depicts a period of profound and multifactorial changes ranging from social to biological factors. While brain development in youths has been studied mostly from an age perspective, recent evidence suggests that pubertal measures may be more sensitive to study adolescent neurodevelopment, however, studies on pubertal timing in relation to brain development are still scarce.MethodsWe investigated if pre- vs. post-menarche status can be classified using machine learning on cortical and subcortical structural magnetic resonance imaging (MRI) data from strictly age-matched adolescent females from the Adolescent Brain Cognitive Development (ABCD) cohort. For comparison of the identified menarche-related patterns to age-related patterns of neurodevelopment, we trained a brain age prediction model on data from the Philadelphia Neurodevelopmental Cohort and applied it to the same ABCD data, yielding differences between predicted and chronological age referred to as brain age gaps. We tested the sensitivity of both these frameworks to measures of pubertal maturation, specifically age at menarche and puberty status.ResultsThe machine learning model achieved moderate but statistically significant accuracy in the menarche classification task, yielding for each subject a class probability ranging from 0 (pre-) to 1 (post- menarche). Comparison to brain age predictions revealed shared and distinct patterns of neurodevelopment captured by both approaches. Continuous menarche class probabilities were positively associated with brain age gaps, but only the menarche class probabilities—not the brain age gaps—were associated with age at menarche.ConclusionsThis study demonstrates the use of a machine learning model to classify menarche status from structural MRI data while accounting for age-related neurodevelopment. Given its sensitivity towards measures of puberty timing, our work suggests that menarche class probabilities may be developed toward an objective brain-based marker of pubertal development.

An Automated Tool to Classify and Transform Unstructured MRI Data into BIDS Datasets.

The increasing use of neuroimaging in clinical research has driven the creation of many large imaging datasets. However, these datasets often rely on inconsistent naming conventions in image file headers to describe acquisition, and time-consuming manual curation is necessary. Therefore, we sought to automate the process of classifying and organizing magnetic resonance imaging (MRI) data according to acquisition types common to the clinical routine, as well as automate the transformation of raw, unstructured images into Brain Imaging Data Structure (BIDS) datasets. To do this, we trained an XGBoost model to classify MRI acquisition types using relatively few acquisition parameters that are automatically stored by the MRI scanner in image file metadata, which are then mapped to the naming conventions prescribed by BIDS to transform the input images to the BIDS structure. The model recognizes MRI types with 99.475% accuracy, as well as a micro/macro-averaged precision of 0.9995/0.994, a micro/macro-averaged recall of 0.9995/0.989, and a micro/macro-averaged F1 of 0.9995/0.991. Our approach accurately and quickly classifies MRI types and transforms unstructured data into standardized structures with little-to-no user intervention, reducing the barrier of entry for clinical scientists and increasing the accessibility of existing neuroimaging data.

Abnormally decreased functional connectivity of the right nucleus basalis of Meynert in Alzheimer’s disease patients with depression symptoms

Dysfunction of the basal forebrain is the main pathological feature in patients with Alzheimer's disease (AD). The aim of this study was to explore whether depressive symptoms cause changes in the functional network of the basal forebrain in AD patients. We collected MRI data from depressed AD patients (n = 24), nondepressed AD patients (n = 14) and healthy controls (n = 20). Resting-state functional magnetic resonance imaging data and functional connectivity analysis were used to study the characteristics of the basal forebrain functional network of the three groups of participants. The functional connectivity differences among the three groups were compared using ANCOVA and post hoc analyses. Compared to healthy controls, depressed AD patients showed reduced functional connectivity between the right nucleus basalis of Meynert and the left supramarginal gyrus and the supplementary motor area. These results increase our understanding of the neural mechanism of depressive symptoms in AD patients.