High-resolution T1-weighted MRI Research Articles

Deep Learning constrained Compressed Sensing Reconstruction Improves High-Resolution 3D T2-weighted Turbo Spin Echo MRI of the Lumbar Spine

ObjectiveWe sought to assess the image quality of 3D T2-weighted (w) Turbo Spin Echo (TSE) sequences with Deep Learning (DL) constrained Compressed Sensing (CS) reconstruction relative to a reference 2D T2w TSE sequence for routine clinical lumbar spine MRI. Materials and Methods53 patients underwent imaging of the lumbar spine with a sagittal 2D T2w TSE sequence and with two CS accelerated 3D T2w TSE sequences (voxel size of 0.4x0.4 × 0.5 mm) with CS factors of 7 and 11. The CS accelerated sequences were reconstructed with iterative reconstruction with wavelet transformation (conventional CS) and secondly with a DL constrained CS reconstruction (named CS-AI). Two readers graded image quality, based on 8 metrics (overall image quality, presence of image noise, presence of motion artifacts, delineation/conspicuity and clarity of anatomical structures as spinal cord, cauda equine nerve roots, cerebrospinal fluid CSF, intervertebral disc, bone marrow and intervertebral foramen) using Likert scales. ResultsOverall interreadout agreement was substantial (Krippendorff's alpha = 0.724, 95%CI: 0.692-0.755). The CS7-AI and CS11-AI sequences were comparable or better than the 2D sequence in all 8 metrics (p<0.001-p>0.99). The CS7 and CS11 sequences were comparable or better than the 2D sequence in only 5 and 3 of 8 metrics respectively (p<0.001-p>0.99). ConclusionA DL constrained CS reconstruction significantly improves the quality of accelerated high-resolution 3D T2w TSE imaging of the lumbar spine. Thus, high quality imaging in sub-millimeter resolution in all three imaging planes can be achieved without compromising the image quality as compared with standard 2D T2w TSE imaging.

Cortex-to-caudate volume ratio as a predictor of cognitive decline in Alzheimer's disease and mild cognitive impairment

BackgroundBrain and cortical atrophy play crucial roles in supporting the clinical diagnosis of Alzheimer's disease (AD). This study hypothesized that the ratios of brain or cortical volume to subcortical gray matter structure volumes are potential imaging markers for cognitive alterations in AD dementia and amnestic mild cognitive impairment (aMCI). MethodsSeventy-seven subjects diagnosed with AD dementia or aMCI underwent baseline neuropsychological testing, 2-year follow-up cognitive assessments, and high-resolution T1-weighted MRI scans. Total brain/cortical volume and subcortical gray matter structure volumes were automatically segmented and measured. Univariate and multiple linear regression analyses were conducted to determine the associations between volumetric ratios and interval changes in cognitive scores. ResultsThe ratio of cortical volume to caudate volume showed the most significant association with changes in MoCA (B = 0.132, SE = 0.042, p = 0.002), MMSE (B = 0.140, SE = 0.040, p = 0.001), and CDR-SOB (B = −0.013, SE = 0.005, p = 0.007) scores over the 2-year follow-up period. These associations remained significant after adjusting for various covariates. Similar associations were observed for the ratios of cortical volume to putamen and globus pallidum volumes. ConclusionsThe cortex-to-caudate volume ratio is significantly associated with cognitive decline in AD dementia and aMCI. This ratio may serve as a useful biomarker for monitoring disease progression and predicting cognitive outcomes. Our findings highlight the importance of considering the relative atrophy of cortical and subcortical structures in understanding AD pathology.

Comparison of detailed brain volume measurements in schizophrenia with healthy individuals

Aims: This study aims to provide insights into the preliminary and definitive diagnosis, treatment modalities, and elucidation of the underlying pathophysiological mechanisms of schizophrenia, alongside ongoing research efforts. Methods: This retrospective study examined a cohort of 31 patients (17 male, 14 female) diagnosed with schizophrenia according to the criteria specified in the Diagnostic and Statistical Manual of Mental Disorders (DSM V) at the Psychiatry Clinic of the University Hospital. The healthy control group, comprising 31 individuals (17 male, 14 female), was selected from archived records at the same hospital, with sociodemographic characteristics matching those of the patient group. High-resolution T1-weighted MRI images of the individuals were analyzed using the VolBrain AssemblyNet program. Results: It has been observed that the volumes of various brain structures, including white matter (WM), gray matter (GM), subcortical GM, cortical GM, WM+GM, IC, total cerebrum, right cerebrum, left cerebrum, cerebrum WM, cerebrum GM, are significantly reduced (p&lt;0.05) in patients diagnosed with schizophrenia compared to healthy controls. Additionally, volumes of cerebellar WM, GM, vermis, brainstem, accumbens, hippocampus, thalamus, ventral diencephalon (DC), amygdala, and basal forebrain were found to be decreased in schizophrenia patients compared to healthy controls. Conversely, volumes of the pallidum, caudate, putamen, inferior lateral ventricle, lateral ventricle, third ventricle, fourth ventricle, and CSF were observed to be increased. Conclusion: These findings underscore the widespread nature of neuroanatomical alterations in schizophrenia and highlight the importance of understanding these changes for elucidating the disorder's pathophysiology and developing targeted therapeutic interventions. Further research is warranted to explore the interplay of genetic, developmental, and environmental factors in schizophrenia.

Identification of patients with internet gaming disorder via a radiomics-based machine learning model of subcortical structures in high-resolution T1-weighted MRI

It is of vital importance to establish an objective and reliable model to facilitate the early diagnosis and intervention of internet gaming disorder (IGD). A total of 133 patients with IGD and 110 healthy controls (HCs) were included. We extracted radiomic features of subcortical structures in high-resolution T1-weighted MRI. Different combinations of four feature selection methods (analysis of variance, Kruskal–Wallis, recursive feature elimination and relief) and ten classification algorithms were used to identify the most robust combined models for distinguishing IGD patients from HCs. Furthermore, a nomogram incorporating radiomic signatures and independent clinical factors was developed. Calibration curve and decision curve analyses were used to evaluate the nomogram. The combination of analysis of variance selector and logistic regression classifier identified that the radiomic model constructed with 20 features from the right caudate nucleus and amygdala showed better IGD screening performance. The radiomic model produced good areas under the curves (AUCs) in the training, validation and test cohorts (AUCs of 0.961, 0.903 and 0.895, respectively). In addition, sex, internet addiction test scores and radiomic scores were included in the nomogram as independent risk factors for IGD. Analysis of the correction curve and decision curve showed that the clinical-radiomic model has good reliability (C-index: 0.987). The nomogram incorporating radiomic features of subcortical structures and clinical characteristics achieved satisfactory classification performance and could serve as an effective tool for distinguishing IGD patients from HCs.

Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology

The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.

Cerebellar white and gray matter abnormalities in temporal lobe epilepsy: a voxel-based morphometry study.

Previous structural neuroimaging studies linked cerebellar deficits to temporal lobe epilepsy (TLE). The functions of various cerebellar regions are increasingly being valued, and their changes in TLE patients warrant further in-depth investigation. In this study, we used the Spatially Unbiased Infratentorial (SUIT) toolbox with a new template to evaluate the cerebellar structural abnormalities in patients with TLE, and further explored the relationship between the changes of different cerebellar regions and cognition. Thirty-two patients with TLE were compared with 39 healthy controls (HC) matched according to age, gender, handedness, and education level. All participants underwent a high-resolution T1-weighted MRI scan on a 3.0 Tesla scanner. We used a voxel-based morphometry (VBM) approach utilizing the SUIT toolbox to provide an optimized and fine-grained exploration of cerebellar structural alterations associated with TLE. Compared with HC, TLE patients showed a significant reduction in the volume of gray matter in the Left lobule VI and white matter in the Right Crus II. In the TLE patient group, we conducted partial correlation analysis between the volumes of different cerebellar regions and cognitive rating scale scores, such as MMSE and MoCA. The volume of the Left lobule VI (GM) exhibited a positive correlation with the MMSE score, but no significant correlation was found with the MoCA score. On the other hand, there was no significant correlation observed between the volume of the Right Crus II (WM) and the two cognitive scale scores mentioned above. Furthermore, it was observed that the MMSE was more effective than the MoCA in identifying epilepsy patients with cognitive impairment. This study supported previous research indicating that temporal lobe epilepsy (TLE) is linked to structural changes in the cerebellum, specifically affecting the volume of both gray and white matter. These findings offer valuable insights into the neurobiology of TLE and hold potential to inform the development of enhanced diagnostic methods and more effective treatment approaches.

Volumetric analysis: Rethinking brain metastases response assessment.

The Response Assessment in Neuro-Oncology for Brain Metastases (RANO-BM) criteria are the gold standard for assessing brain metastases (BMs) treatment response. However, they are limited by their reliance on 1D, despite the routine use of high-resolution T1-weighted MRI scans for BMs, which allows for 3D measurements. Our study aimed to investigate whether volumetric measurements could improve the response assessment in patients with BMs. We retrospectively evaluated a dataset comprising 783 BMs and analyzed the response of 185 of them from 132 patients who underwent stereotactic radiotherapy between 2007 and 2021 at 5 hospitals. We used T1-weighted MRIs to compute the volume of the lesions. For the volumetric criteria, progressive disease was defined as at least a 30% increase in volume, and partial response was characterized by a 20% volume reduction. Our study showed that the proposed volumetric criteria outperformed the RANO-BM criteria in several aspects: (1) Evaluating every lesion, while RANO-BM failed to evaluate 9.2% of them. (2) Classifying response effectively in 140 lesions, compared to only 72 lesions classified by RANO-BM. (3) Identifying BM recurrences a median of 3.3 months earlier than RANO-BM criteria. Our study demonstrates the superiority of volumetric criteria in improving the response assessment of BMs compared to the RANO-BM criteria. Our proposed criteria allow for evaluation of every lesion, regardless of its size or shape, better classification, and enable earlier identification of progressive disease. Volumetric criteria provide a standardized, reliable, and objective tool for assessing treatment response.

Developing Topics.

Hippocampal formation atrophy is a well-established imaging biomarker of several neurological diseases, including Alzheimer's disease, temporal lobe epilepsy, and schizophrenia. The hippocampus is divided into subfields that have different functions and vary in sensitivity to different diseases. This study investigates the potential interaction between COVID-19 and the various hippocampus subfields, which may shed light on the long-term neurological consequences of the virus. We obtained high-resolution T1-weighted (T1w) and T2-weighted (T2w) MRI images using 7T scanners located at three sites in two countries: Pittsburgh (n = 14) and Texas (San Antonio and Houston) (n = 40) in the USA, and Nottingham, UK (n = 33). We evaluated the hippocampus subfields using the ASHS package [1-3]. Imaging sets of 51 subjects with minimal or no manual segmentation corrections (Figures 1 and 2) were included in the analysis. We conducted T-tests with Bonferroni correction, adjusting for age and intracranial volume to identify the differences in hippocampus subfield volumes across groups. Participants who needed admission into the ICU due to Covid-19 showed a significantly lower (p-value = 0.0034) left CA1 volume compared to participants who did not require ICU (Figure 3). In addition, several other non-significant trends were observed. Our preliminary findings suggest that Covid-19 may impact the hippocampus, particularly in patients who required intensive care. However, the study - as of to date - has a small sample size and lacks a comparison group with patients who were admitted into ICU for acute illnesses other than Covid-19. Additionally, longitudinal data is needed to track the long-term effects of the disease on the hippocampal subfields. NIH R56AG074467, R01MH111265 and R01AG063525

25 High-resolution MRI Reveals Selective Patterns of Hippocampal Subfield Atrophy in Focal Epilepsy

Objective:Hippocampal pathology is a consistent feature in persons with temporal lobe epilepsy (TLE) and a strong biomarker of memory impairment. Histopathological studies have identified selective patterns of cell loss across hippocampal subfields in TLE, the most common being cellular loss in the cornu ammonis 1 (CA1) and dentage gyrus (DG). Structural neuroimaging provides a non-invasive method to understand hippocampal pathology, but traditionally only at a whole-hippocampal level. However, recent methodological advances have enabled the non-invasive quantification of subfield pathology in patients, enabling potential integration into clinical workflow. In this study, we characterize patterns of hippocampal subfield atrophy in patients with TLE and examine the associations between subfield atrophy and clinical characteristics.Participants and Methods:High-resolution T2 and T1-weighted MRI were collected from 31 participants (14 left TLE; 6 right TLE; 11 healthy controls [HC], aged 18-61 years). Reconstructions of hippocampal subfields and estimates of their volumes were derived using the Automated Segmentation of Hippocampal Subfields (ASHS) pipeline. Total hippocampal volume was calculated by combining estimates of the subfields CA1-3, DG, and subiculum. To control for variations in head size, all volume estimates were divided by estimates of total brain volume. To assess disease effects on hippocampal atrophy, hippocampi were recoded as either ipsilateral or contralateral to the side of seizure focus. Two sample t-tests at a whole-hippocampus level were used to test for ipsilateral and contralateral volume loss in patients relative to HC. To assess whether we replicated the selective histopathological patterns of subfield atrophy, we carried out mixed-effects ANOVA, coding for an interaction between diagnostic group and hippocampal subfield. Finally, to assess effects of disease load, non-parametric correlations were performed between subfield volume and age of first seizure and duration of illness.Results:Patients had significantly smaller total ipsilateral hippocampal volume compared with HC (d=1.23, p&lt;.005). Contralateral hippocampus did not significantly differ between TLE and HC. Examining individual subfields for the ipsilateral hemisphere revealed significant main-effects for group (F(1, 29)=8.2, p&lt;0.01), subfields (F(4, 115)=550.5, p&lt;0.005), and their interaction (F(4, 115)=8.1, p&lt;0.001). Post-hoc tests revealed that TLE had significantly smaller volume in the ipsilateral CA1 (d=-2.0, p&lt;0.001) and DG (d = -1.4, p&lt;0.005). Longer duration of illness was associated with smaller volume of ipsilateral CA2 (p=-0.492, p&lt;0.05) and larger volume of contralateral whole-hippocampus (p=0.689, p&lt;0.001), CA1 (p=0.614, p &lt; 0.005), and DG (p=0.450, p&lt;0.05).Conclusions:Histopathological characterization after surgery has revealed important associations between hippocampal subfield cell loss and memory impairments in patients with TLE. Here we demonstrate that non-invasive neuroimaging can detect a pattern of subfield atrophy in TLE (i.e., CA1/DG) that matches the most common form of histopathologically-observed hippocampal sclerosis in TLE (HS Type 1) and has been linked directly to both verbal and visuospatial memory impairment. Finally, we found evidence that longer disease duration is associated with larger contralateral hippocampal volume, driven by increases in CA1 and DG. This may reflect subfield-specific functional reorganization to the unaffected brain tissue, a compensatory effect which may have important implications for patient function and successful treatment outcomes.

Dose-response in modulating brain function with transcranial direct current stimulation: From local to network levels.

Understanding the dose-response relationship is crucial in studying the effects of brain stimulation techniques, such as transcranial direct current stimulation (tDCS). The dose-response relationship refers to the relationship between the received stimulation dose and the resulting response, which can be described as a function of the dose at various levels, including single/multiple neurons, clusters, regions, or networks. Here, we are focused on the received stimulation dose obtained from computational head models and brain responses which are quantified by functional magnetic resonance imaging (fMRI) data. In this randomized, triple-blind, sham-controlled clinical trial, we recruited sixty participants with methamphetamine use disorders (MUDs) as a sample clinical population who were randomly assigned to receive either sham or active tDCS. Structural and functional MRI data, including high-resolution T1 and T2-weighted MRI, resting-state functional MRI, and a methamphetamine cue-reactivity task fMRI, were acquired before and after tDCS. Individual head models were generated using the T1 and T2-weighted MRI data to simulate electric fields. In a linear approach, we investigated the associations between electric fields (received dose) and changes in brain function (response) at four different levels: voxel level, regional level (using atlas-based parcellation), cluster level (identifying active clusters), and network level (task-based functional connectivity). At the voxel level, regional level, and cluster level, no FDR-corrected significant correlation was observed between changes in functional activity and electric fields. However, at the network level, a significant positive correlation was found between frontoparietal connectivity and the electric field at the frontopolar stimulation site (r = 0.42, p corrected = 0.02; medium effect size). Our proposed pipeline offers a methodological framework for analyzing tDCS effects by exploring dose-response relationships at different levels, enabling a direct link between electric field variability and the neural response to tDCS. The results indicate that network-based analysis provides valuable insights into the dependency of tDCS neuromodulatory effects on the individual's regional current dose. Integration of dose-response relationships can inform dose optimization, customization, or the extraction of predictive/treatment-response biomarkers in future brain stimulation studies.

Highly active antiretroviral therapy-related effects on morphological connectivity in HIV.

Suboptimal concentration of the antiretroviral drug is insufficient to inhibit HIV destruction on brain structure and function due to the resistance of blood brain barrier. We aimed to investigate highly active antiretroviral therapy (HAART)-related effects on the morphological connectivity in people with HIV (PWH). Case-control study. Fifty-five HAART-treated for more than 3 months and 54 untreated PWH, as well as 66 demographically matched healthy controls underwent a high-resolution 3D T1-weighted MRI. Individual-level morphological brain network based on gray matter volume of 90 brain regions was constructed and network topological properties were analyzed. Network-based statistics (NBS) was performed to identify sub-networks showing significant differences in morphological connectivity. Correlation and mediation analyses were employed to evaluate associations between the morphological properties and clinical variables of PWH. Although PWH exhibited small-world architecture in their morphological brain networks, untreated PWH demonstrated altered network properties while HAART-treated PWH showed relatively similar network properties compared to healthy controls. Furthermore, HAART-related effects were mainly involved the bilateral putamen and left thalamus. The findings of NBS further indicated the cortico-striatum-thalamic-cortical loop was involved in the therapeutic-associated morphological network. The positive correlations between the HAART treatment and nodal degree and efficiency of the putamen were mediated by the number of CD4 + T lymphocytes. The topological properties are recovered to normal in PWH after HAART and the effects induced by HAART are mostly within the cortical-subcortical circuit.

Dehydroepiandrosterone mediates associations between trauma-related symptoms and anterior pituitary volume in children and adolescents.

The anterior pituitary gland (PG) is a potential locus of hypothalamic-pituitary-adrenal (HPA) axis responsivity to early life stress, with documented associations between dehydroepiandrosterone (DHEA) levels and anterior PG volumes. In adults, elevated anxiety/depressive symptoms are related to diminished DHEA levels, and studies have shown a positive relationship between DHEA and anterior pituitary volumes. However, specific links between responses to stress, DHEA levels, and anterior pituitary volume have not been established in developmental samples. High-resolution T1-weighted MRI scans were collected from 137 healthy youth (9-17 years; Mage = 12.99 (SD = 1.87); 49% female; 85% White, 4% Indigenous, 1% Asian, 4% Black, 4% multiracial, 2% not reported). The anterior and posterior PGs were manually traced by trained raters. We examined the mediating effects of salivary DHEA on trauma-related symptoms (i.e., anxiety, depression, and posttraumatic) and PG volumes as well as an alternative model examining mediating effects of PG volume on DHEA and trauma-related symptoms. DHEA mediated the association between anxiety symptoms and anterior PG volume. Specifically, higher anxiety symptoms related to lower DHEA levels, which in turn were related to smaller anterior PG. These results shed light on the neurobiological sequelae of elevated anxiety in youth and are consistent with adult findings showing suppressed levels of DHEA in those with greater comorbid anxiety and depression. Specifically, adolescents with greater subclinical anxiety may exhibit diminished levels of DHEA during the pubertal window, which may be associated with disruptions in anterior PG growth.

Quantitative Analysis of BrainAge using Neuroimaging Data to Estimate Radiation-Induced Brain Aging and Associations with Cognitive Changes in Brain Metastases Patients

Radiotherapy is critical in brain tumor management yet causes accelerated aging via irreparable damage to white matter, cortex, and subcortical areas. BrainAGE is an open-source machine learning tool that analyses morphology from raw quantitative structural MRI to generate a predictive 'BrainAge' value, enabling analyses of patients with neurodegenerative diseases. We assessed the utility of this algorithm in measuring brain age morphology in brain metastases patients on a clinical trial, analyzed differences in BrainAge and chronological age, and explored associations with cognitive performance. We analyzed pre-SRS high-resolution T1-weighted noncontrast volumetric MRI in patients with brain metastases (n = 57, median age 63 years, range 20-87) treated on a prospective clinical trial of cognitive-sparing SRS. The BrainAGE machine learning tool processed images to derive a BrainAge; it reliably generates this value using a Gaussian Processes regression, with a robust neurotypical training cohort. We examined correlations between BrainAge and chronological age with regression. Brain-predicted age gap (Brain-Gap) was calculated, defined as the difference between a patient's BrainAge and chronological age. We assessed cognitive function at baseline pre-SRS with a comprehensive battery of validated tests across multiple domains performed by a neuropsychologist, including memory (HVLT total), executive function (COWAT category fluency), and language (COWAT letter fluency). Raw test scores were scaled to T-scores adjusting for age, sex, and education level per testing norms. We examined if Brain-Gap was associated with cognitive performance pre-SRS. BrainAGE software was robust, estimating a BrainAge for all input MRI sequences. We found a strong correlation between BrainAge and chronological age (r-squared = 0.82). The Brain-Gap median value was 0.84 and the mean was -0.90, in years (range -18.30-11.36 years). Brain-Gap was not significantly associated with cognitive performance across the three tests at the pre-SRS timepoint. Among patients with impaired cognition (T<35), there was no significant difference in BrainAge relative to chronological age. To our knowledge, this is the first study utilizing the BrainAGE model as a biomarker of neuroanatomical age for brain tumors patients. Existing publications had lesion-free cohorts. Our results show that BrainAge was reliably associated with chronologic age in brain metastases patients pre-SRS. The Brain-Gap difference was minimal at baseline, and we found no significant association with pre-SRS cognitive performance. Future studies will examine this biomarker after SRS to look for individual aging radiation effects and prediction for cognitive changes after therapy.

Diffusion MRI data analysis assisted by deep learning synthesized anatomical images (DeepAnat).

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 T1-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.

Structural gray matter alterations in glioblastoma and high-grade glioma-A potential biomarker of survival.

Patients with glioblastoma (GBM) and high-grade glioma (HGG, World Health Organization [WHO] grade IV glioma) have a poor prognosis. Consequently, there is an unmet clinical need for accessible and noninvasively acquired predictive biomarkers of overall survival in patients. This study evaluated morphological changes in the brain separated from the tumor invasion site (ie, contralateral hemisphere). Specifically, we examined the prognostic value of widespread alterations of cortical thickness (CT) in GBM/HGG patients. We used FreeSurfer, applied with high-resolution T1-weighted MRI, to examine CT, evaluated prior to standard treatment with surgery and chemoradiation in patients (GBM/HGG, N = 162, mean age 61.3 years) and 127 healthy controls (HC; 61.9 years mean age). We then compared CT in patients to HC and studied patients' associated changes in CT as a potential biomarker of overall survival. Compared to HC cases, patients had thinner gray matter in the contralesional hemisphere at the time of tumor diagnosis. patients had significant cortical thinning in parietal, temporal, and occipital lobes. Fourteen cortical parcels showed reduced CT, whereas in 5, it was thicker in patients' cases. Notably, CT in the contralesional hemisphere, various lobes, and parcels was predictive of overall survival. A machine learning classification algorithm showed that CT could differentiate short- and long-term survival patients with an accuracy of 83.3%. These findings identify previously unnoticed structural changes in the cortex located in the hemisphere contralateral to the primary tumor mass. Observed changes in CT may have prognostic value, which could influence care and treatment planning for individual patients.

Accelerated 3D high-resolution T2-weighted breast MRI with deep learning constrained compressed sensing, comparison with conventional T2-weighted sequence on 3.0 T

PurposeTo evaluate the feasibility of isotropic 3D high-resolution T2-weighted imaging (T2WI) MRI sequences and compare the images reconstructed by integrating artificial intelligence-compressed sensing (AI-CS), compressed sensing (CS), and conventional 2D T2WI sequences for quality. Materials and methodsFifty-two female patients (ages: 26–80 years) with suspected breast cancer were enrolled. They underwent breast MRI examinations using three sequences: conventional T2WI, CS 3D T2WI, and AI-CS 3D T2WI. Image quality, signal-to-noise ratio (SNR), contrast-to-noise ratio, tumor volume, and maximal tumor diameter were compared using the Friedman test. Image quality was scored on a 5-point scale, with 1 indicating nonassessable quality and 5 indicating excellent quality. Tumor volume and maximal tumor diameter were compared based on AI-CS 3D T2WI (slightly high signal), conventional T2WI, and dynamic contrast-enhanced (DCE) sequences. ResultsAll three T2WI were successfully performed in all patients. 3D CS and AI-CS were significantly better than conventional T2WI in terms of lesion conspicuity and morphology, structural details, overall image quality, diagnostic information for breast lesions, and breast tissue delineation (P < 0.001). The SNR of conventional T2WI was significantly higher for 3D T2WI sequences. The contrast-to-noise ratio was significantly higher for AI-CS 3D T2WI than for conventional T2WI sequence. There was no significant difference in tumor volume between DCE (8.08 ± 16.51) and AI-CS 3D T2WI (8.25 ± 16.29) sequences and no significant differences in tumor diameter among DCE, AI-CS 3D T2WI, and conventional T2WI sequences. ConclusionIsotropic-resolution 3D T2WI sequences can be acquired using AI-CS while maintaining image quality and diagnostic value, which may pave the way for isotropic 3D high-resolution T2WI for clinical application.

An Open MRI Dataset For Multiscale Neuroscience

Multimodal neuroimaging grants a powerful window into the structure and function of the human brain at multiple scales. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends (also referred to as gradients) in brain microstructure and connectivity, offering an integrative framework to study multiscale brain organization. Here, we share a multimodal MRI dataset for Microstructure-Informed Connectomics (MICA-MICs) acquired in 50 healthy adults (23 women; 29.54 ± 5.62 years) who underwent high-resolution T1-weighted MRI, myelin-sensitive quantitative T1 relaxometry, diffusion-weighted MRI, and resting-state functional MRI at 3 Tesla. In addition to raw anonymized MRI data, this release includes brain-wide connectomes derived from (i) resting-state functional imaging, (ii) diffusion tractography, (iii) microstructure covariance analysis, and (iv) geodesic cortical distance, gathered across multiple parcellation scales. Alongside, we share large-scale gradients estimated from each modality and parcellation scale. Our dataset will facilitate future research examining the coupling between brain microstructure, connectivity, and function. MICA-MICs is available on the Canadian Open Neuroscience Platform data portal (https://portal.conp.ca) and the Open Science Framework (https://osf.io/j532r/).

Scene memory and hippocampal volume in middle-aged women with early hormone loss

The present study explored whether early midlife bilateral salpingo-oophorectomy (BSO), a female-specific risk factor for dementia, is associated with reduced medial temporal lobe structure and function. Younger middle-aged women with the BRCA1/2 mutation and a BSO prior to spontaneous menopause (SM) were recruited. We determined the performance of women with BSO not taking estradiol-based hormone therapy (n = 18) on a task measuring object and scene recognition and quantified medial temporal lobe subregion volumes using manually segmented high-resolution T2-weighted MRI scans. Comparisons were made to those with BSO taking estradiol-based hormone therapy (n = 20), age-matched premenopausal controls (n = 28), and older women in SM not taking hormone therapy matched for duration of hormone deprivation (n = 17). Reduced hippocampal integrity specific to the BSO group not taking hormone therapy was observed, reflected by significantly smaller dentate gyrus/CA2/CA3 volumes and lower scene recognition memory performance. These findings show that hippocampal subfield volume may be useful for identifying early midlife changes in women at elevated risk for dementia.

Cortical remodeling before and after successful temporal lobe epilepsy surgery.

To explore dynamic alterations of cortical thickness before and after successful anterior temporal lobectomy (ATL) in patients with unilateral mesial temporal lobe epilepsy (mTLE). High-resolution T1-weighted MRI was obtained in 28 mTLE patients who achieved seizure freedom for at least 24months after ATL and 29 healthy controls. Patients were scanned at five timepoints, including before surgery, 3, 6, 12 and 24months after surgery. Preoperative cortical thickness of mTLE patients were compared with healthy controls. Dynamic alterations of cortical thickness before and after surgery were compared among five scans using linear mixed models. Patients with mTLE showed cortical thinning pre-surgically in ipsilateral entorhinal cortex, parahippocampal gyrus, inferior parietal cortex, lateral occipital cortex; contralateral pericalcarine cortex (PCC); and bilateral caudal middle frontal gyrus (cMFG), paracentral lobule, precentral gyrus (PCG), superior parietal cortex. Cortical thickening was observed in contralateral rostral anterior cingulate cortex (rACC). Patients showed postsurgical cortical thinning in ipsilateral temporal lobe, fusiform gyrus, caudal anterior cingulate cortex, lingual gyrus, and insula. Ipsilateral cMFG, PCC, and contralateral PCG showed significant cortical thickening after surgery. In addition, contralateral rACC showed cortical thickening at 3 months follow-up, however, with obvious cortical thinning at 24months follow-up. Mesial temporal lobe epilepsy patients showed widespread cortical thinning before and after anterior temporal lobectomy. Progressive cortical thinning mainly existed in neighboring regions of resection. Postoperative cortical thickening may indicate cortical remodeling after successful surgery.

FV 11 Physical activity at youth is associated with the strength of connectivity in subcortical locomotor and motor networks: A resting-state fMRI study

Background: In the face of aging and neurological disease some people show better motor function than others despite similar structural preconditions of the nervous system, suggesting the existence of a reserve that enables them to compensate for damages. Previous work in stroke patients has identified connections between regions of the subcortical locomotor and motor networks that are strongly linked to better motor outcome. Objective: To identify associations between resting-state functional connectivity (rs-FC) between regions of the subcortical locomotor and motor networks, performance in a visuomotor adaptation task, and current as well as past physical activity. Methods: In this preliminary study, 11 neurologically healthy individuals (mean age: 43 [21-63]; 55% female) were included. High-resolution T1-weighted MRI was acquired to quantify brain atrophy, and resting-state functional connectivity (rsFC) of the motor network was assessed. A visuomotor adaptation task (VAT) was used as a measure of current adaptive performance. The main measure of adaptive performance were the rates of adaptation and deadaptation. This was measured as the rate of change in error between the start of the adaptation/deadaptation phase and the plateau. The plateau range was determined as the mean ± standard deviation of the last 32 trials. Plateaus were defined as the trial in which subsequent 5 trials fell within the plateau range. Additionally, subjects reported their current and past physical activity. Relationships between regional brain volumes, rsFC values, VAT measures, and reported physical activity were evaluated using Spearman’s partial correlations with age as a covariate. Results: Physical activity between ages 14 and 21 was strongly correlated with connectivity between right premotor area (PMA) and left supplementary motor area (SMA) (r=0.89, p=0.001), right PMA and left M1 (r=0.85, p=0.004), left SMA and left PMA (r=0.85, p<0.001), and pedunculopontine nucleus (PPN) and right superior frontal gyrus (SFG) (r=0.85, p<0.001). The rate of deadaptation positively correlated with the volume of the right M1 and right SMA (r=0.70, p=0.023; r=0.69, p=0.027). Conclusion: The present pilot study indicates that physical activity at ages 14-21 may turn out to be an important determinant of individual motor network efficiency. Further steps of analysis will be done to investigate the link between locomotor adaptation rates and motor adaptive capacity. This may ultimately enable us to assess quantitative aspects of the individual motor reserve and to further understand its contribution to successful adaptation to ageing and disease.