From imaging signals to physical measurements
Magnetic resonance imaging and positron emission tomography provide complementary observations of tissue structure, physiology and molecular processes. Our research develops the physical models and computational methods needed to interpret these signals quantitatively. MRI work spans acquisition physics, diffusion and perfusion measurements, and the estimation of tissue parameters. PET research examines tracer kinetics and the relationship between a measured time course and the biological processes that generate it. The aim is to obtain interpretable measurements whose meaning remains clear across acquisitions, participants and experimental settings.
Diffusion MRI is a particular focus because water displacement provides indirect information about tissue organisation at scales below the image resolution. We investigate microstructural and non-Gaussian diffusion models, alongside high-resolution anatomical imaging, to characterise variation in neural tissue. These methods support studies of cortical and brainstem structure, glial responses and longitudinal change. Model parameters are interpreted in relation to their assumptions and complementary measurements, rather than treated as direct observations of a single cellular feature.
Combining modalities and evaluating quantitative methods
Multimodal analysis brings MRI, PET and other measurements into a shared framework while preserving what each modality measures. Registration, reconstruction, image fusion and parameter estimation connect anatomical context with functional and molecular information. Dynamic measurements also allow the analysis of transport, perfusion and temporal responses. Work on nanoparticle imaging applies related principles to the estimation of spatial distributions, concentration and clearance.
Reproducibility is central to this programme. Quantitative methods must account for acquisition settings, preprocessing choices, measurement noise and uncertainty in fitted parameters. Benchmarking and comparisons across datasets help distinguish stable tissue information from variation introduced by an analysis pipeline. This provides a methodological basis for longitudinal studies and for integrating measurements collected using different instruments or protocols.
Research resources
Profiles by research interest
Additional profiles by research interest (10)
Research projects
Additional research projects (13)
Publications
2026
arXiv
A prominent theory in cognitive science suggests that concepts in the brain are organized as high-dimensional vectors, with semantic meaning captured by directions and relative angles in this space. Brain decoding is the effort of reconstructing or retrieving stimuli (or their representations) from neural activity and involves finding a function that approxi…Read the full abstractCollapse abstract
A prominent theory in cognitive science suggests that concepts in the brain are organized as high-dimensional vectors, with semantic meaning captured by directions and relative angles in this space. Brain decoding is the effort of reconstructing or retrieving stimuli (or their representations) from neural activity and involves finding a function that approximates how the brain represents concepts. This motivates the investigation of contrastive objectives as biologically plausible candidates to reverse the brain loss function. In this work, we study how functional MRI (fMRI) activity can generally be mapped with the embedding spaces of foundation models in vision, language, and audio. Although neural computations are highly non-linear at the microscale, fMRI measurements average signals across space and time, further smoothed by noise, effectively linearizing the observable representation. Consistent with these views, our experiments across multiple datasets demonstrate that linear contrastive decoders consistently outperform ridge regression and standard non-linear alternatives, and that these results generalize across images, text, and sound. These findings indicate that decoding gains arise more from the choice of training objective than from architectural complexity, pointing to contrastive-linear models as a principled strategy for brain decoding.
2026
Frontiers in bioengineering and biotechnology
The CROSSBRAIN EU project aims to address the heterogeneous nature of brain pathologies by developing wireless implantable microbots (µBots, planned dimensions 100 × 100 × 100 μm3) for highly localized neuromodulation. These devices are designed to precisely modulate brain activity with minimal invasiveness, enabling targeted resolution of specific spatiotem…Read the full abstractCollapse abstract
The CROSSBRAIN EU project aims to address the heterogeneous nature of brain pathologies by developing wireless implantable microbots (µBots, planned dimensions 100 × 100 × 100 μm3) for highly localized neuromodulation. These devices are designed to precisely modulate brain activity with minimal invasiveness, enabling targeted resolution of specific spatiotemporal events, capabilities not currently achieved by existing neuromodulation technologies. A crucial step involves visualizing and ensuring the optimal placement of the µBots in the brain tissue, to study their functionality after implantation. In this preliminary ex vivo study, we used non-functional µBot silicon (Si) dummies matching the lateral dimensions of the intended µBots, with reduced thickness (100 × 100 × 50 μm3) to simplify fabrication and handling. Due to the intrinsic MRI incompatibility of the µBot platform, encompassing both the dummies used in this study and the future functional devices under development, and the limitations of standard histological approaches in reliably identifying and preserving the implant site during processing, we developed an integrated imaging workflow combining 2D and 3D techniques. While standard histological methods and tissue clearing presented substantial limitations in preserving the position of the dummies within the brain tissue, combining histological techniques with 3D X-ray tomography provided a robust strategy. In particular, synchrotron radiation-based X-ray Phase Contrast Tomography (XPCT), with its intrinsic high contrast and resolution, enabled detailed visualization of dummies within the surrounding vascular and cellular architecture. In contrast, conventional micro-Computed Tomography (micro-CT), although more widely accessible, enabled non-destructive guidance for targeted sectioning. Importantly, and in line with the scope of a Brief Research Report, this study presents a preliminary but technically robust investigation conducted within the CROSSBRAIN project, aimed at identifying and establishing an optimized imaging strategy for the visualization of implanted µBots in brain tissue. This methodological framework is intended as an initial step toward future in vivo studies, in which the validated imaging pipeline will be applied to track both dummy and functional devices and to enable subsequent evaluation of foreign body response under physiologically relevant conditions. This ex vivo workflow therefore provides the essential technical foundation for such future investigations and supports the clear positioning of this work as a feasibility and optimization study. This approach could be particularly valuable for new generations of implantable technologies incompatible with MRI and could support future development of personalized neuromodulation therapies by enabling precise device localization and structural tissue assessment.
2026
Nanoscale advances
Magnetic nanodiscs (MNDs) represent a transformative class of anisotropic magnetic nanoparticles with intrinsic vortex magnetization, enabling multifunctional applications in biomedical imaging and therapy. Here, we demonstrate their potential as dual-mode magnetic resonance (MR) contrast agents, a unique feature which is enabled by the high longitudinal rel…Read the full abstractCollapse abstract
Magnetic nanodiscs (MNDs) represent a transformative class of anisotropic magnetic nanoparticles with intrinsic vortex magnetization, enabling multifunctional applications in biomedical imaging and therapy. Here, we demonstrate their potential as dual-mode magnetic resonance (MR) contrast agents, a unique feature which is enabled by the high longitudinal relaxivity (r 1 ≈ 40 mM-1 s-1) at ultralow magnetic fields (<70 µT) in combination with strong transverse relaxivity (r 2 > 150 mM-1 s-1) at ultrahigh fields (>7 T). This field-dependent relaxivity profile uniquely positions MNDs as versatile T 1/T 2 agents compatible with emerging low-field MRI platforms and high-resolution clinical systems. Ex vivo and in vivo assessments confirmed clear anatomical localization and preferential hepatic accumulation, suggesting prolonged circulation times due to surface-mediated immune evasion. These properties highlight MNDs as promising candidates for next-generation theranostics, with tunable magnetic responses, high contrast efficiency, and the ability to synergize imaging and neurostimulation.
2026
AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: In multiple sclerosis, the choroid plexus is thought to promote and sustain the disease immunopathologic inflammatory process. However, its association with cortical pathology and disease progression is still uncertain. We aimed to characterize choroid plexus enlargement and evolution in MS and its relationship with imaging markers of…Read the full abstractCollapse abstract
BACKGROUND AND PURPOSE: In multiple sclerosis, the choroid plexus is thought to promote and sustain the disease immunopathologic inflammatory process. However, its association with cortical pathology and disease progression is still uncertain. We aimed to characterize choroid plexus enlargement and evolution in MS and its relationship with imaging markers of compartmentalized inflammation and clinical outcome.
MATERIALS AND METHODS: One hundred patients with MS (74 relapsing-remitting [RRMS]; 26 secondary-progressive MS [SPMS] and 41 matched healthy controls (HC) underwent 3T MRI to measure brain volumes and 7T MRI for cortical and WM lesions, including paramagnetic rim lesion (PRL) segmentation. Choroid plexus volumes of the lateral ventricles were manually edited from FreeSurfer segmentation by 1 neurologist and normalized by total intracranial volume to estimate the choroid plexus ratio (CPR). The Expanded Disability Status Scale was assessed at baseline and longitudinally in 71 patients, 43 of whom had a follow-up MRI at 1.5 years. An extreme gradient boosting algorithm estimated the importance of CPR, alongside other imaging markers, in predicting disease phenotype (RRMS versus SPMS) and progression independent of relapse activity.
RESULTS: Relative to HC, CPR was higher in patients (11.66 [3.21] versus 14.98 [4.37] × 10-4, P = .001), both in RRMS and SPMS (P = .007, P = .003), and there was a mean ∼4% increase per year in CPR at follow-up, though it was not significant[ (P = .14). Choroid plexus enlargement correlated with greater PRL and cortical lesion volumes (r = 0.35, P < .001). CPR did not discriminate MS phenotype but predicted progression independent of relapse activity alongside CSF subarachnoid space volume, PRL, and cortical lesion volumes (median [median absolute deviation] area under the curve 0.71 [0.12], accuracy 0.74 [0.10], sensitivity 0.81 [0.12], and specificity of 0.52 [0.22]).
CONCLUSIONS: In MS, a CPR increase relates to imaging markers of compartmentalized disease activity including cortical lesions and PRLs and is a critical predictor of disease progression. Our findings could provide the rationale for implementing CPR estimation for prognosis prediction in MS.
2026
Neuroimage. Reports
INTRODUCTION: This study aims to identify early brain network changes in de novo Parkinson's disease (PD) using resting state-functional Magnetic Resonance Imaging (rs-fMRI), graph-theoretical analysis, and a functional brain network disruption index (k), applied here for the first time to de novo PD. MATERIALS AND METHODS: The study enrolled untreated de no…Read the full abstractCollapse abstract
INTRODUCTION: This study aims to identify early brain network changes in de novo Parkinson's disease (PD) using resting state-functional Magnetic Resonance Imaging (rs-fMRI), graph-theoretical analysis, and a functional brain network disruption index (k), applied here for the first time to de novo PD. MATERIALS AND METHODS: The study enrolled untreated de novo PD patients and age- and sex-matched healthy controls. PD patients underwent comprehensive clinical assessments (MDS-UPDRS III, H&Y, MMSE, MoCA, NMSS). MRI data were acquired on a 3T system, including 3D T1-weighted MPRAGE and rs-fMRI. rs-fMRI data were pre-processed and analysed using graph theory. RESULTS: The study included 30 de novo PD patients and 30 healthy controls. While global network metrics did not differ significantly, local metrics revealed a reduced disruption index k in de novo PD patients. The disruption index k was negatively correlated with MMSE scores and demonstrated strong discriminatory power between PD patients and healthy controls based on clustering coefficient metrics. Significant differences in hub regions were found, as some disappeared in PD patients while others emerged compared to healthy controls. CONCLUSIONS: This study provides evidence of widespread functional alterations in the local brain networks of de novo Parkinson's disease (PD) patients, suggesting early reorganization of brain connectivity. The disruption index (k) demonstrated the ability to detect early and subtle changes in functional brain networks in de novo Parkinson patients. SIGNIFICANCE: rs-fMRI can provide valuable insights into the early stages of PD pathophysiology helping to understand the complexity of PD.
Additional publications (27)
2026
2026
2025
European journal of nuclear medicine and molecular imaging
PURPOSE: Here, we combined a longitudinal design to assess whole-brain hyper- and hypo-connectivity in the different clinical phases of Alzheimer's disease (AD) with a multimodal approach to understand how such connectivity changes were related to glucose hypometabolism.
METHODS: We selected a longitudinal cohort of N = 66 subjects with clinical, cerebrospi…Read the full abstractCollapse abstract
PURPOSE: Here, we combined a longitudinal design to assess whole-brain hyper- and hypo-connectivity in the different clinical phases of Alzheimer's disease (AD) with a multimodal approach to understand how such connectivity changes were related to glucose hypometabolism.
METHODS: We selected a longitudinal cohort of N = 66 subjects with clinical, cerebrospinal fluid and FDG-PET assessments, from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. N = 31 AD individuals were assessed at three stages: mild cognitive impairment (AD-MCI, T0), early phase of dementia (mild-AD, T1) and dementia (AD-D, T2). We included N = 35 age/sex-matched healthy controls. We assessed longitudinal metabolic connectivity using Pearson's correlation, clustering analysis and graph theory metrics.
RESULTS: In the MCI-AD stages, hypo- and hyper-connectivity coexisted. Data-driven, longitudinal clustering analysis identified specific pathological clusters: a default mode network cluster, with prevalent hypo-connectivity and severe, persistent hypometabolism; a limbic cluster showing hyper-connectivity and steeper metabolic decline. Metabolism in hyper-connected limbic regions showed a mediation effect on worsening of AD-like parieto-temporal hypometabolism and predicted faster conversion to dementia.
CONCLUSION: Hypo- and hyper-connectivity, especially in early stages, may have different roles in AD neurodegenerative processes, with metabolism in hyper-connected regions acting as a mediator on the neurodegeneration of core regions of AD pathology.
2025
Frontiers in neuroinformatics
INTRODUCTION: Neuroinflammation, a pathophysiological process involved in numerous disorders, is typically imaged using [11C]PBR28 (or TSPO) PET. However, this technique is limited by high costs and ionizing radiation, restricting its widespread clinical use. MRI, a more accessible alternative, is commonly used for structural or functional imaging, but when…Read the full abstractCollapse abstract
INTRODUCTION: Neuroinflammation, a pathophysiological process involved in numerous disorders, is typically imaged using [11C]PBR28 (or TSPO) PET. However, this technique is limited by high costs and ionizing radiation, restricting its widespread clinical use. MRI, a more accessible alternative, is commonly used for structural or functional imaging, but when used using traditional approaches has limited sensitivity to specific molecular processes. This study aims to develop a deep learning model to generate TSPO PET images from structural MRI data collected in human subjects.
METHODS: A total of 204 scans, from participants with knee osteoarthritis (n = 15 scanned once, 15 scanned twice, 14 scanned three times), back pain (n = 40 scanned twice, 3 scanned three times), and healthy controls (n = 28, scanned once), underwent simultaneous 3 T MRI and [11C]PBR28 TSPO PET scans. A 3D U-Net model was trained on 80% of these PET-MRI pairs and validated using 5-fold cross-validation. The model's accuracy in reconstructed PET from MRI only was assessed using various intensity and noise metrics.
RESULTS: The model achieved a low voxel-wise mean squared error (0.0033 ± 0.0010) across all folds and a median contrast-to-noise ratio of 0.0640 ± 0.2500 when comparing true to reconstructed PET images. The synthesized PET images accurately replicated the spatial patterns observed in the original PET data. Additionally, the reconstruction accuracy was maintained even after spatial normalization.
DISCUSSION: This study demonstrates that deep learning can accurately synthesize TSPO PET images from conventional, T1-weighted MRI. This approach could enable low-cost, noninvasive neuroinflammation imaging, expanding the clinical applicability of this imaging method.
2025
Brain, behavior, and immunity
Recent evidence suggests that chronic pain patients exhibit elevated brain levels of the neuroinflammation marker 18 kDa translocator protein (TSPO). However, the clinical significance of brain TSPO elevations, and their responses to pain interventions, remain unknown. To explore these questions, we studied patients with knee osteoarthritis (KOA) undergoing…Read the full abstractCollapse abstract
Recent evidence suggests that chronic pain patients exhibit elevated brain levels of the neuroinflammation marker 18 kDa translocator protein (TSPO). However, the clinical significance of brain TSPO elevations, and their responses to pain interventions, remain unknown. To explore these questions, we studied patients with knee osteoarthritis (KOA) undergoing total knee arthroplasty (TKA), a procedure which is curative for most, but carries a relatively high risk of persistent post-surgical pain. Pre-surgical KOA patients (n = 41) and healthy controls (n = 22) underwent brain positron emission tomography/magnetic resonance imaging, using the TSPO radioligand [11C]PBR28. A subset of KOA patients (n = 27) returned for a second scan one-year post-TKA. When compared groups, pre-surgical KOA patients exhibited widespread [11C]PBR28 PET signal elevations (Standardized Uptake Value Ratio), with pituitary uptake positively correlating with knee pain severity (rho = 0.51; p = 0.003). A voxel-wise paired t-test revealed that while most brain regions showed no change post-surgery, the [11C]PBR28 PET signal significantly decreased in the thalamus and caudate, reaching control levels. Additionally, a Support Vector Machine model based on pre-surgical imaging, clinical, and demographic features, achieved a correlation of rho = 0.487 (p = 0.001) between the predicted and actual pain improvement. Top predictive features included [11C]PBR28 uptake in the pituitary gland, cuneal cortex, amygdala and other regions. This study suggests that neuroinflammation 1) is widespread in KOA and, in some regions, 2) is linked to pain severity, 3) undergoes normalization following TKA, and 4) can predict post-surgical TKA outcomes. Understanding the neuroinflammatory mechanisms in KOA and post-surgical pain may guide targeted interventions and improve patient outcomes.
2025
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Quantifying the volume of distribution (VT) in Positron Emission Tomography (PET) is widely considered the gold standard for assessing tracer binding. However, this process requires an accurate estimation of the tracer's input function (IF) obtained through arterial sampling and metabolite correction-procedures that are both invasive and technically demandin…Read the full abstractCollapse abstract
Quantifying the volume of distribution (VT) in Positron Emission Tomography (PET) is widely considered the gold standard for assessing tracer binding. However, this process requires an accurate estimation of the tracer's input function (IF) obtained through arterial sampling and metabolite correction-procedures that are both invasive and technically demanding. To overcome these limitations, we introduce a neural network-based framework for estimating the IF directly from [11C]PBR28 dynamic PET data, exploring generalisability across datasets and scanners. The framework employs a patched variational autoencoder (pVAE) for dimensionality reduction, generating IFs with uncertainty (NNIF-dPET), and computes VT from the mean output signal. Additionally, we evaluate two alternative methods: NNIF-IDIF, which derives IFs from image-derived input functions, and NNIF-unBlood, which uses uncorrected blood signals as input. NNIF-dPET achieves accuracy comparable to true arterial IFs while outperforming IDIF-based methods, suggesting that latent space representations can effectively approximate whole-blood activity for parent plasma input function estimation, rather than relying on pre-selected voxels.Clinical Relevance-This approach highlights the potential for scalable, non-invasive PET quantification across diverse clinical settings.
2025
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Brain metastases (BM), along with primary central nervous system lymphomas and glioblastomas, represent the majority of malignant brain tumors encountered in clinical neuro-oncology, driving a need for advanced imaging techniques and post-processing methods to improve their characterization and treatment monitoring. In particular, stereotactic radiosurgery (…Read the full abstractCollapse abstract
Brain metastases (BM), along with primary central nervous system lymphomas and glioblastomas, represent the majority of malignant brain tumors encountered in clinical neuro-oncology, driving a need for advanced imaging techniques and post-processing methods to improve their characterization and treatment monitoring. In particular, stereotactic radiosurgery (SRS), a cornerstone treatment for BM, delivers high-dose, focused radiation (>20 Gy) to target lesions with minimal impact on surrounding tissues. Despite its efficacy, radiation-induced effects such as early radiation effects (ERE) and adverse radiation effects (ARE) complicate diagnosis and management, with ARE occurring in up to 30% of patients, often presenting as ring-enhancing T2/FLAIR hyperintensities. To address these challenges, we aimed to compare standard radiomics-based machine learning approaches with pretrained generative models for assessing ERE in BM lesions. A cohort of 21 patients for a total of 35 lesions (17 treatment-naïve and 18 post-SRS +/- combination therapy) who underwent multiparametric 18F-FPIA PET/MRI was analyzed. The study investigated: 1) Multiparametric analysis of PET and MRI diffusion/perfusion parameters (ADC, Ktrans, CBF, K1, vt); 2) MRI-based radiomics; 3) static PET radiomics; 4) Dynomics; 5) a combination of PET and MRI radiomics; and 6) low-level embeddings from a pretrained generative diffusion model applied to full T1, static PET, and their combination. Using manually contoured lesion masks for analyses 1-5 and lesion-free embeddings for analysis 6, multiple classifiers (SVM, XGBoost, Linear regressor) were applied after feature standardization and principal component analysis (retaining 90% variance). Fivefold cross-validation demonstrated comparable performances across radiomic approaches (Accuracy: 71.95±0.05%, AUC: 0.72±0.05%), while the pretrained generative model achieved significantly higher performance (Accuracy: 83.82±0.01%, AUC: 0.83±0.01%) without requiring lesion segmentation in assessing ERE in BM lesions.Clinical Relevance-This study shows the potential of generative models to streamline and enhance the assessment of early radiation effects in parenchymal metastatic lesions without need of lesion segmentation.
2025
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The creation of synthetic medical data that truly captures the statistical distribution of real-world patient information, while simultaneously protecting individual privacy, remains a formidable challenge for the clinical and scientific community. This challenge is especially pronounced in nuclear medicine research, where rigorous data sharing is hindered b…Read the full abstractCollapse abstract
The creation of synthetic medical data that truly captures the statistical distribution of real-world patient information, while simultaneously protecting individual privacy, remains a formidable challenge for the clinical and scientific community. This challenge is especially pronounced in nuclear medicine research, where rigorous data sharing is hindered by tight regulations and ethical considerations. In this study, we introduce a multimodal deep learning model designed to reconstruct (and thereby enable future synthesis of) 123I-FPCIT SPECT (DaT) scans by leveraging both the target DaT and co-registered T1-weighted MRI scans. Through extensive experimentation on a large Parkinson's Progression Markers Initiative (PPMI) dataset, comprising healthy controls, Parkinson's disease (PD) patients, and individuals without imaging evidence of dopaminergic deficits (SWEDD), we demonstrate that the proposed framework yields DaT images that strongly preserve clinical signal distributions. Our findings show minimal intensity discrepancies, unbiased contrast-to-noise ratios, and robust region-based analyses across pathological and demographic subgroups, highlighting the feasibility of this approach for large-scale data augmentation in neurodegenerative research.Clinical Relevance-By enabling the reconstruction of DaT scans from multi-contrast inputs, this framework has the potential to enhance future generation of synthetic data that are applicable to early PD detection, disease progression studies, and model training in clinical scenarios where data are often limited, and privacy constraints are stringent.
2025
2024
Computer methods and programs in biomedicine
INTRODUCTION: We propose a novel approach for the non-invasive quantification of dynamic PET imaging data, focusing on the arterial input function (AIF) without the need for invasive arterial cannulation.
METHODS: Our method utilizes a combination of three-dimensional depth-wise separable convolutional layers and a physically informed deep neural network to…Read the full abstractCollapse abstract
INTRODUCTION: We propose a novel approach for the non-invasive quantification of dynamic PET imaging data, focusing on the arterial input function (AIF) without the need for invasive arterial cannulation.
METHODS: Our method utilizes a combination of three-dimensional depth-wise separable convolutional layers and a physically informed deep neural network to incorporatea priori knowledge about the AIF's functional form and shape, enabling precise predictions of the concentrations of [11C]PBR28 in whole blood and the free tracer in metabolite-corrected plasma.
RESULTS: We found a robust linear correlation between our model's predicted AIF curves and those obtained through traditional, invasive measurements. We achieved an average cross-validated Pearson correlation of 0.86 for whole blood and 0.89 for parent plasma curves. Moreover, our method's ability to estimate the volumes of distribution across several key brain regions – without significant differences between the use of predicted versus actual AIFs in a two-tissue compartmental model – successfully captures the intrinsic variability related to sex, the binding affinity of the translocator protein (18 kDa), and age.
CONCLUSIONS: These results not only validate our method's accuracy and reliability but also establish a foundation for a streamlined, non-invasive approach to dynamic PET data quantification. By offering a precise and less invasive alternative to traditional quantification methods, our technique holds significant promise for expanding the applicability of PET imaging across a wider range of tracers, thereby enhancing its utility in both clinical research and diagnostic settings.
2024
eLife
Axonal degeneration is a central pathological feature of multiple sclerosis and is closely associated with irreversible clinical disability. Current noninvasive methods to detect axonal damage in vivo are limited in their specificity and clinical applicability, and by the lack of proper validation. We aimed to validate an MRI framework based on multicompartm…Read the full abstractCollapse abstract
Axonal degeneration is a central pathological feature of multiple sclerosis and is closely associated with irreversible clinical disability. Current noninvasive methods to detect axonal damage in vivo are limited in their specificity and clinical applicability, and by the lack of proper validation. We aimed to validate an MRI framework based on multicompartment modeling of the diffusion signal (AxCaliber) in rats in the presence of axonal pathology, achieved through injection of a neurotoxin damaging the neuronal terminal of axons. We then applied the same MRI protocol to map axonal integrity in the brain of multiple sclerosis relapsing-remitting patients and age-matched healthy controls. AxCaliber is sensitive to acute axonal damage in rats, as demonstrated by a significant increase in the mean axonal caliber along the targeted tract, which correlated with neurofilament staining. Electron microscopy confirmed that increased mean axonal diameter is associated with acute axonal pathology. In humans with multiple sclerosis, we uncovered a diffuse increase in mean axonal caliber in most areas of the normal-appearing white matter, preferentially affecting patients with short disease duration. Our results demonstrate that MRI-based axonal diameter mapping is a sensitive and specific imaging biomarker that links noninvasive imaging contrasts with the underlying biological substrate, uncovering generalized axonal damage in multiple sclerosis as an early event.
2024
Social Cognitive and Affective Neuroscience
Previous research on the neurobiological bases of resilience in youth has largely used categorical definitions of resilience and voxel-based morphometry methods that assess gray matter volume. However, it is important to consider brain structure more broadly as different cortical properties have distinct developmental trajectories. To address these limitatio…Read the full abstractCollapse abstract
Previous research on the neurobiological bases of resilience in youth has largely used categorical definitions of resilience and voxel-based morphometry methods that assess gray matter volume. However, it is important to consider brain structure more broadly as different cortical properties have distinct developmental trajectories. To address these limitations, we used surface-based morphometry and data-driven, continuous resilience scores to examine associations between resilience and cortical structure. Structural MRI data from 286 youths (Mage = 13.6 years, 51% female) who took part in the European multi-site FemNAT-CD study were pre-processed and analyzed using surface-based morphometry. Continuous resilience scores were derived for each participant based on adversity exposure and levels of psychopathology using the residual regression method. Vertex-wise analyses assessed for correlations between resilience scores and cortical thickness, surface area, gyrification and volume. Resilience scores were positively associated with right lateral occipital surface area and right superior frontal gyrification and negatively correlated with left inferior temporal surface area. Moreover, sex-by-resilience interactions were observed for gyrification in frontal and temporal regions. Our findings extend previous research by revealing that resilience is related to surface area and gyrification in frontal, occipital and temporal regions that are implicated in emotion regulation and face or object recognition.
2024
Magnetic Resonance in Medicine
PURPOSE: K trans $$ {K}^{\mathrm{trans}} $$ has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for K trans $$ {K}^{\mathrm{trans}} $$ quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Co…Read the full abstractCollapse abstract
PURPOSE: K trans $$ {K}^{\mathrm{trans}} $$ has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for K trans $$ {K}^{\mathrm{trans}} $$ quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Contrast-Enhanced (OSIPI-DCE) challenge was designed to benchmark methods to better help the efforts to standardize K trans $$ {K}^{\mathrm{trans}} $$ measurement. METHODS: A framework was created to evaluate K trans $$ {K}^{\mathrm{trans}} $$ values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for K trans $$ {K}^{\mathrm{trans}} $$ quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' K trans $$ {K}^{\mathrm{trans}} $$ values, the applied software, and a standard operating procedure. These were evaluated using the proposed OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score defined with accuracy, repeatability, and reproducibility components. RESULTS: Across the 10 received submissions, the OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0-1 = lowest-highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in K trans $$ {K}^{\mathrm{trans}} $$ analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility. CONCLUSIONS: This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within K trans $$ {K}^{\mathrm{trans}} $$ estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with different benchmarking methodology.
2023
Journal of Personalized Medicine
Traditional imaging techniques for breast cancer (BC) diagnosis and prediction, such as X-rays and magnetic resonance imaging (MRI), demonstrate varying sensitivity and specificity due to clinical and technological factors. Consequently, positron emission tomography (PET), capable of detecting abnormal metabolic activity, has emerged as a more effective tool…Read the full abstractCollapse abstract
Traditional imaging techniques for breast cancer (BC) diagnosis and prediction, such as X-rays and magnetic resonance imaging (MRI), demonstrate varying sensitivity and specificity due to clinical and technological factors. Consequently, positron emission tomography (PET), capable of detecting abnormal metabolic activity, has emerged as a more effective tool, providing critical quantitative and qualitative tumor-related metabolic information. This study leverages a public clinical dataset of dynamic 18F-Fluorothymidine (FLT) PET scans from BC patients, extending conventional static radiomics methods to the time domain-termed as 'Dynomics'. Radiomic features were extracted from both static and dynamic PET images on lesion and reference tissue masks. The extracted features were used to train an XGBoost model for classifying tumor versus reference tissue and complete versus partial responders to neoadjuvant chemotherapy. The results underscored the superiority of dynamic and static radiomics over standard PET imaging, achieving accuracy of 94% in tumor tissue classification. Notably, in predicting BC prognosis, dynomics delivered the highest performance, achieving accuracy of 86%, thereby outperforming both static radiomics and standard PET data. This study illustrates the enhanced clinical utility of dynomics in yielding more precise and reliable information for BC diagnosis and prognosis, paving the way for improved treatment strategies.
2023
Human brain mapping
Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) have been previously used to explore white matter related to human immunodeficiency virus (HIV) infection. While DTI and DKI suffer from low specificity, the Combined Hindered and Restricted Model of Diffusion (CHARMED) provides additional microstructural specificity. We used these three mod…Read the full abstractCollapse abstract
Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) have been previously used to explore white matter related to human immunodeficiency virus (HIV) infection. While DTI and DKI suffer from low specificity, the Combined Hindered and Restricted Model of Diffusion (CHARMED) provides additional microstructural specificity. We used these three models to evaluate microstructural differences between 35 HIV-positive patients without neurological impairment and 20 healthy controls who underwent diffusion-weighted imaging using three b-values. While significant group effects were found in all diffusion metrics, CHARMED and DKI analyses uncovered wider involvement (80% vs. 20%) of all white matter tracts in HIV infection compared with DTI. In restricted fraction (FR) analysis, we found significant differences in the left corticospinal tract, middle cerebellar peduncle, right inferior cerebellar peduncle, right corticospinal tract, splenium of the corpus callosum, left superior cerebellar peduncle, left superior cerebellar peduncle, pontine crossing tract, left posterior limb of the internal capsule, and left/right medial lemniscus. These are involved in language, motor, equilibrium, behavior, and proprioception, supporting the functional integration that is frequently impaired in HIV-positivity. Additionally, we employed a machine learning algorithm (XGBoost) to discriminate HIV-positive patients from healthy controls using DTI and CHARMED metrics on an ROIwise basis, and unique contributions to this discrimination were examined using Shapley Explanation values. The CHARMED and DKI estimates produced the best performance. Our results suggest that biophysical multishell imaging, combining additional sensitivity and built-in specificity, provides further information about the brain microstructural changes in multimodal areas involved in attentive, emotional and memory networks often impaired in HIV patients.
2022
Journal of Pharmacy and Pharmacognosy Research
Context: The current COVID-19 pandemic has significantly impacted health and socio-economic status worldwide. The only way to combat this situation is to develop an effective vaccine and immunize people around the globe. Aims: To construct a multi-epitope spike glycoprotein-based vaccine from the SARS-CoV-2 Surabaya isolate using a bioinformatics approach. M…Read the full abstractCollapse abstract
Context: The current COVID-19 pandemic has significantly impacted health and socio-economic status worldwide. The only way to combat this situation is to develop an effective vaccine and immunize people around the globe. Aims: To construct a multi-epitope spike glycoprotein-based vaccine from the SARS-CoV-2 Surabaya isolate using a bioinformatics approach. Methods: The spike protein was submitted to IEDB, VaxiJen, AllerTOP, and ToxinPred webservers to predict antigenic, non-allergic, non-toxic, B- and T-cell epitopes. To develop a multi-epitope vaccine, an adjuvant cholera toxin B subunit was linked to B-cell and B-cell with T-cell through EAAAK and GPGPG linkers, respectively. The designed vaccine 3D structure development, refinement, and validation were done through PHYRE2, Galaxy Refine, and RAMPAGE webservers. Moreover, the Cluspro-2.0 webserver was used for the molecular docking of the vaccine designed with TLR3. The vaccine+TLR3 complex was docked with Surfactant protein A as a control to validate the docking results. Finally, immune-simulation and in silico cloning of the vaccine were carried out by C-ImmSim webserver and SnapGene software, respectively. Results: A multi-epitopic vaccine containing B and T-cell was developed using 392 amino acids with a molecular weight of 40825.59 Da. The docking and immunogenicity results of the vaccine met all established parameters for constructing a quality vaccine. Furthermore, the optimized sequence of the vaccine was successfully cloned in expression vector pET 28 a (+) that yielded a colon of 2724 bp. Conclusions: The vaccine’s immunogenicity demonstrates its effectiveness against SARS-CoV-2 infection. Further confirmatory testing may therefore be performed as soon as possible in the public interest.
2021
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Diffusion tensor imaging (DTI) has been used to explore changes in the brain of subjects with human immunodeficiency virus (HIV) infection. However, DTI notoriously suffers from low specificity. Neurite orientation dispersion and density imaging (NODDI) is a compartmental model able to provide specific microstructural information with additional sensitivity/…Read the full abstractCollapse abstract
Diffusion tensor imaging (DTI) has been used to explore changes in the brain of subjects with human immunodeficiency virus (HIV) infection. However, DTI notoriously suffers from low specificity. Neurite orientation dispersion and density imaging (NODDI) is a compartmental model able to provide specific microstructural information with additional sensitivity/specificity. In this study we use both the NODDI and the DTI models to evaluate microstructural differences between 35 HIV-positive patients and 20 healthy controls. Diffusion-weighted imaging was acquired using three b-values (0, 1000 and 2500 s/mm2). Both DTI and NODDI models were fitted to the data, obtaining estimates for fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), neurite density index (NDI) and orientation dispersion index (ODI), after which we performed group comparisons using Tract-based spatial statistics (TBSS). While significant group effects were found in in FA, MD, RD, AD and NDI, NDI analysis uncovered a much wider involvement of brain tissue in HIV infection as compared to DTI. In region-of interest (ROI)-based analysis, NDI estimates from the right corticospinal tract produced excellent performance in discriminating the two groups (AUC = 0.974, sensitivity = 90%; specificity =97%).
2021
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
While Granger causality (GC) has been often employed in network neuroscience, most GC applications are based on linear multivariate autoregressive (MVAR) models. However, real-life systems like biological networks exhibit notable nonlinear behaviour, hence undermining the validity of MVAR-based GC (MVAR-GC). Most nonlinear GC estimators only cater for additi…Read the full abstractCollapse abstract
While Granger causality (GC) has been often employed in network neuroscience, most GC applications are based on linear multivariate autoregressive (MVAR) models. However, real-life systems like biological networks exhibit notable nonlinear behaviour, hence undermining the validity of MVAR-based GC (MVAR-GC). Most nonlinear GC estimators only cater for additive nonlinearities or, alternatively, are based on recurrent neural networks or long short-term memory networks, which present considerable training difficulties and tailoring needs. We reformulate the GC framework in terms of echo-state networks-based models for arbitrarily complex networks, and characterize its ability to capture nonlinear causal relations in a network of noisy Duffing oscillators, showing a net advantage of echo state GC (ES-GC) in detecting nonlinear, causal links. We then explore the structure of ES-GC networks in the human brain employing functional MRI data from 1003 healthy subjects drawn from the human connectome project, demonstrating the existence of previously unknown directed within-brain interactions. In addition, we examine joint brain-heart signals in 15 subjects where we explore directed interaction between brain networks and central vagal cardiac control in order to investigate the so-called central autonomic network in a causal manner. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.
2021
Journal of neuroimaging : official journal of the American Society of Neuroimaging
BACKGROUND AND PURPOSE: To investigate the reorganization of the central nervous system provided by resting state-functional MRI (rs-fMRI), graph-theoretical analysis, and a newly developed functional brain network disruption index in patients with human immunodeficiency virus (HIV) infection. METHODS: Forty HIV-positive patients without neurological impairm…Read the full abstractCollapse abstract
BACKGROUND AND PURPOSE: To investigate the reorganization of the central nervous system provided by resting state-functional MRI (rs-fMRI), graph-theoretical analysis, and a newly developed functional brain network disruption index in patients with human immunodeficiency virus (HIV) infection. METHODS: Forty HIV-positive patients without neurological impairment and 20 age- and sex-matched healthy controls underwent rs-fMRI at 3T; blood sampling was obtained the same day to evaluate biochemical variables (absolute, relative, and nadir CD4 T-lymphocytes value and plasmatic HIV-RNA). From fMRI data, disruption indices, as well as global and local graph theoretical measures, were estimated and examined for group differences (HIV vs. controls) as well as for associations with biochemical variables (HIV only). Finally, all data (global and local graph-theoretical measures, disruption indices, and biochemical variables) were tested for putative differences across three patient groups based on the duration of combined antiretroviral therapy (cART). RESULTS: Brain function of HIV patients appeared to be deeply reorganized as compared to normal controls. The disruption index showed significant negative association with relative CD4 values, and a positive significant association between plasmatic HIV-RNA and local graph-theoretical metrics in the left lingual gyrus and the right lobule IV and V of right cerebellar hemisphere was also observed. Finally, a differential distribution of HIV clinical biomarkers and several brain metrics was observed across cART duration groups. CONCLUSION: Our study demonstrates that rs-fMRI combined with advanced graph theoretical analysis and disruption indices is able to detect early and subtle functional changes of brain networks in HIV patients.
2020
Frontiers in Immunology
Neuroinflammation commences decades before Alzheimer's disease (AD) clinical onset and represents one of the earliest pathomechanistic alterations throughout the AD continuum. Large-scale genome-wide association studies point out several genetic variants-TREM2, CD33, PILRA, CR1, MS4A, CLU, ABCA7, EPHA1, and HLA-DRB5-HLA-DRB1-potentially linked to neuroinflam…Read the full abstractCollapse abstract
Neuroinflammation commences decades before Alzheimer's disease (AD) clinical onset and represents one of the earliest pathomechanistic alterations throughout the AD continuum. Large-scale genome-wide association studies point out several genetic variants-TREM2, CD33, PILRA, CR1, MS4A, CLU, ABCA7, EPHA1, and HLA-DRB5-HLA-DRB1-potentially linked to neuroinflammation. Most of these genes are involved in proinflammatory intracellular signaling, cytokines/interleukins/cell turnover, synaptic activity, lipid metabolism, and vesicle trafficking. Proteomic studies indicate that a plethora of interconnected aberrant molecular pathways, set off and perpetuated by TNF-α, TGF-β, IL-1β, and the receptor protein TREM2, are involved in neuroinflammation. Microglia and astrocytes are key cellular drivers and regulators of neuroinflammation. Under physiological conditions, they are important for neurotransmission and synaptic homeostasis. In AD, there is a turning point throughout its pathophysiological evolution where glial cells sustain an overexpressed inflammatory response that synergizes with amyloid-β and tau accumulation, and drives synaptotoxicity and neurodegeneration in a self-reinforcing manner. Despite a strong therapeutic rationale, previous clinical trials investigating compounds with anti-inflammatory properties, including non-steroidal anti-inflammatory drugs (NSAIDs), did not achieve primary efficacy endpoints. It is conceivable that study design issues, including the lack of diagnostic accuracy and biomarkers for target population identification and proof of mechanism, may partially explain the negative outcomes. However, a recent meta-analysis indicates a potential biological effect of NSAIDs. In this regard, candidate fluid biomarkers of neuroinflammation are under analytical/clinical validation, i.e., TREM2, IL-1β, MCP-1, IL-6, TNF-α receptor complexes, TGF-β, and YKL-40. PET radio-ligands are investigated to accomplish in vivo and longitudinal regional exploration of neuroinflammation. Biomarkers tracking different molecular pathways (body fluid matrixes) along with brain neuroinflammatory endophenotypes (neuroimaging markers), can untangle temporal-spatial dynamics between neuroinflammation and other AD pathophysiological mechanisms. Robust biomarker-drug codevelopment pipelines are expected to enrich large-scale clinical trials testing new-generation compounds active, directly or indirectly, on neuroinflammatory targets and displaying putative disease-modifying effects: novel NSAIDs, AL002 (anti-TREM2 antibody), anti-Aβ protofibrils (BAN2401), and AL003 (anti-CD33 antibody). As a next step, taking advantage of breakthrough and multimodal techniques coupled with a systems biology approach is the path to pursue for developing individualized therapeutic strategies targeting neuroinflammation under the framework of precision medicine.
2020
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In 2019, approximately 38 million people were living with human immunodeficiency virus (HIV). Combined antiretroviral therapy (cART) has determined a change in the course of HIV infection, transforming it into a chronic condition which results in cumulative exposure to antiretroviral drugs, inflammatory effects and aging. Relatedly, at least one quarter of H…Read the full abstractCollapse abstract
In 2019, approximately 38 million people were living with human immunodeficiency virus (HIV). Combined antiretroviral therapy (cART) has determined a change in the course of HIV infection, transforming it into a chronic condition which results in cumulative exposure to antiretroviral drugs, inflammatory effects and aging. Relatedly, at least one quarter of HIV-infected patients suffer from cognitive, motor and behavioral disorder, globally known as HIV-associated neurocognitive disorders (HAND). In this context, objective, neuroimaging-based biomarkers are therefore highly desirable in order to detect, quantify and monitor HAND in all disease stages. In this study, we employed functional MRI in conjunction with graph-theoretical analysis as well as a newly developed functional brain network disruption index to assess a putative functional reorganization in HIV positive patients. We found that brain function of HIV patients is deeply reorganized as compared to normal controls. Interestingly, the regions in which we found reorganized hubs are integrated into neuronal networks involved in working memory, motor and executive functions often altered in patients with HAND. Overall, our study demonstrates that rs-fMRI combined with advanced graph theoretical analysis and disruption indices is able to detect early, subtle functional changes of brain networks in HIV patients before structural changes become evident.
2020
Frontiers in Physics
Since its discovery in 2001, the interest in the low-intensity focused ultrasound (FUS)-mediated blood-brain barrier (BBB) disruption to deliver genes and drugs to brain tissue has increased steadily. Increasingly sophisticated sonication protocols and dedicated hardware are being developed to efficiently and safely permeabilize the BBB, and novel magnetic r…Read the full abstractCollapse abstract
Since its discovery in 2001, the interest in the low-intensity focused ultrasound (FUS)-mediated blood-brain barrier (BBB) disruption to deliver genes and drugs to brain tissue has increased steadily. Increasingly sophisticated sonication protocols and dedicated hardware are being developed to efficiently and safely permeabilize the BBB, and novel magnetic resonance (MR)-based technologies have been designed to guide FUS-induced BBB opening protocols. MR imaging (MRI) allows not only to more precisely target brain regions and evaluate the outcome of sonication in terms of enhanced BBB permeability but also to control the effects of ultrasound on brain structure and function. This review summarizes the state of the art in current MRI hardware and methods used in BBB opening protocols both in preclinical and clinical settings.
2019
Neuroimage
Some forms of mild cognitive impairment (MCI) are the clinical precursors of Alzheimer's disease (AD), while other MCI types tend to remain stable over-time and do not progress to AD. To identify and choose effective and personalized strategies to prevent or slow the progression of AD, we need to develop objective measures that are able to discriminate the M…Read the full abstractCollapse abstract
Some forms of mild cognitive impairment (MCI) are the clinical precursors of Alzheimer's disease (AD), while other MCI types tend to remain stable over-time and do not progress to AD. To identify and choose effective and personalized strategies to prevent or slow the progression of AD, we need to develop objective measures that are able to discriminate the MCI patients who are at risk of AD from those MCI patients who have less risk to develop AD. Here, we present a novel deep learning architecture, based on dual learning and an ad hoc layer for 3D separable convolutions, which aims at identifying MCI patients who have a high likelihood of developing AD within 3 years. Our deep learning procedures combine structural magnetic resonance imaging (MRI), demographic, neuropsychological, and APOe4 genetic data as input measures. The most novel characteristics of our machine learning model compared to previous ones are the following: 1) our deep learning model is multi-tasking, in the sense that it jointly learns to simultaneously predict both MCI to AD conversion as well as AD vs. healthy controls classification, which facilitates relevant feature extraction for AD prognostication; 2) the neural network classifier employs fewer parameters than other deep learning architectures which significantly limits data-overfitting (we use ∼550,000 network parameters, which is orders of magnitude lower than other network designs); 3) both structural MRI images and their warp field characteristics, which quantify local volumetric changes in relation to the MRI template, were used as separate input streams to extract as much information as possible from the MRI data. All analyses were performed on a subset of the database made publicly available via the Alzheimer's Disease Neuroimaging Initiative (ADNI), (n = 785 participants, n = 192 AD patients, n = 409 MCI patients (including both MCI patients who convert to AD and MCI patients who do not covert to AD), and n = 184 healthy controls). The most predictive combination of inputs were the structural MRI images and the demographic, neuropsychological, and APOe4 data. In contrast, the warp field metrics were of little added predictive value. The algorithm was able to distinguish the MCI patients developing AD within 3 years from those patients with stable MCI over the same time-period with an area under the curve (AUC) of 0.925 and a 10-fold cross-validated accuracy of 86%, a sensitivity of 87.5%, and specificity of 85%. To our knowledge, this is the highest performance achieved so far using similar datasets. The same network provided an AUC of 1 and 100% accuracy, sensitivity, and specificity when classifying patients with AD from healthy controls. Our classification framework was also robust to the use of different co-registration templates and potentially irrelevant features/image portions. Our approach is flexible and can in principle integrate other imaging modalities, such as PET, and diverse other sets of clinical data. The convolutional framework is potentially applicable to any 3D image dataset and gives the flexibility to design a computer-aided diagnosis system targeting the prediction of several medical conditions and neuropsychiatric disorders via multi-modal imaging and tabular clinical data.
2019
Frontiers in Neuroscience
Despite extensive neuroimaging research of primary sensory cortices involved in auditory and visual functions, subcortical structures within these domains, such as the inferior and superior colliculi, the medial and lateral geniculate nuclei and the superior olivary complex, are currently understudied with magnetic resonance imaging (MRI) in living humans. T…Read the full abstractCollapse abstract
Despite extensive neuroimaging research of primary sensory cortices involved in auditory and visual functions, subcortical structures within these domains, such as the inferior and superior colliculi, the medial and lateral geniculate nuclei and the superior olivary complex, are currently understudied with magnetic resonance imaging (MRI) in living humans. This is because a precise localization of these nuclei is hampered by the limited contrast and sensitivity of conventional neuroimaging methods for deep brain nuclei. In this work, we used 7 Tesla multi-modal (T2-weighted and diffusion fractional anisotropy) 1.1 mm isotropic resolution MRI to achieve high sensitivity and contrast for single-subject brainstem and thalamic nuclei delineation. After precise coregistration to stereotactic space, we generated an in vivo human probabilistic atlas of auditory (medial geniculate nucleus, inferior colliculus, and superior olivary complex) and visual (lateral geniculate nucleus and superior colliculus) subcortical nuclei. We foresee the use of this atlas as a tool to precisely identify the location and shape of auditory/visual deep nuclei in research as well as clinical human studies.
2019
Entropy
A growing number of studies are focusing on methods to estimate and analyze the functional connectome of the human brain. Graph theoretical measures are commonly employed to interpret and synthesize complex network-related information. While resting state functional MRI (rsfMRI) is often employed in this context, it is known to exhibit poor reproducibility,…Read the full abstractCollapse abstract
A growing number of studies are focusing on methods to estimate and analyze the functional connectome of the human brain. Graph theoretical measures are commonly employed to interpret and synthesize complex network-related information. While resting state functional MRI (rsfMRI) is often employed in this context, it is known to exhibit poor reproducibility, a key factor which is commonly neglected in typical cohort studies using connectomics-related measures as biomarkers. We aimed to fill this gap by analyzing and comparing the inter- and intra-subject variability of connectivity matrices, as well as graph-theoretical measures, in a large (n = 1003) database of young healthy subjects which underwent four consecutive rsfMRI sessions. We analyzed both directed (Granger Causality and Transfer Entropy) and undirected (Pearson Correlation and Partial Correlation) time-series association measures and related global and local graph-theoretical measures. While matrix weights exhibit a higher reproducibility in undirected, as opposed to directed, methods, this difference disappears when looking at global graph metrics and, in turn, exhibits strong regional dependence in local graphs metrics. Our results warrant caution in the interpretation of connectivity studies, and serve as a benchmark for future investigations by providing quantitative estimates for the inter- and intra-subject variabilities in both directed and undirected connectomic measures.
2016
Clinical Neuroradiology
In recent years many papers about diagnostic applications of diffusion tensor imaging (DTI) have been published. This is because DTI allows to evaluate in vivo and in a non-invasive way the process of diffusion of water molecules in biological tissues. However, the simplified description of the diffusion process assumed in DTI does not permit to completely m…Read the full abstractCollapse abstract
In recent years many papers about diagnostic applications of diffusion tensor imaging (DTI) have been published. This is because DTI allows to evaluate in vivo and in a non-invasive way the process of diffusion of water molecules in biological tissues. However, the simplified description of the diffusion process assumed in DTI does not permit to completely map the complex underlying cellular components and structures, which hinder and restrict the diffusion of water molecules. These limitations can be partially overcome by means of diffusion kurtosis imaging (DKI). The aim of this paper is the description of the theory of DKI, a new topic of growing interest in radiology. DKI is a higher order diffusion model that is a straightforward extension of the DTI model. Here, we analyze the physics underlying this method, we report our MRI acquisition protocol with the preprocessing pipeline used and the DKI parametric maps obtained on a 1.5 T scanner, and we review the most relevant clinical applications of this technique in various neurological diseases.
2009
Journal of Psychiatric Research
BACKGROUND: Transcranial magnetic stimulation (TMS) is an attractive research and possibly therapeutic tool for non-invasive central nervous system stimulation. However, relatively little is known about the direction, magnitude and distribution of induced electric field and current flows in tissue, and optimal setup characteristics as well as appropriate sha…Read the full abstractCollapse abstract
BACKGROUND: Transcranial magnetic stimulation (TMS) is an attractive research and possibly therapeutic tool for non-invasive central nervous system stimulation. However, relatively little is known about the direction, magnitude and distribution of induced electric field and current flows in tissue, and optimal setup characteristics as well as appropriate sham stimulation conditions remain largely undetermined, hampering reproducibility. METHODS: We reconstruct the conductive phenomena induced by TMS by implementing digitized coil geometry and realistic stimulator parameters and solving the electromagnetic problem over an MRI-based, realistic head model of 1mm resolution. Findings are validated by recording motor evoked potentials from the right abductor pollicis brevis muscle from healthy subjects stimulated in a stereotaxic framework. RESULTS: Several commonly used sham stimulation configurations elicit conductive patterns which achieve up to 40% of the strength of real stimulation. Also, variations in coil position of the order of a 7 degrees tilt, which are expected to occur in non-stereotaxic stimulation, can alter the stimulation intensity by up to 25%. CONCLUSIONS: In accordance with our findings, several clinical studies observe measurable effects during sham stimulation or no significant difference between sham and real stimulation, and the sensitivity of stimulation intensity to tiny coil rotations affords a partial explanation for the poor reproducibility and partial disagreements observed across clinical TMS studies. Knowledge of coil and stimulator specifications alone is hence not sufficient to control stimulation conditions, and a stereotaxic setup coupled with individually adjusted field solvers appear essential in performing reliable TMS studies.