Fernando Calamante’s research while affiliated with The University of Sydney and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (259)


Fibre Population-guided Pre-training for 3D Spatial Super-Resolution on Multimodal Brain Diffusion MR Imaging
  • Conference Paper

December 2024

·

3 Reads

Zihao Tang

·

Xinyi Wang

·

Mariano Cabezas

·

[...]

·


Fig. 2. Performance of the model using different window sizes. (a) The MSE of the models trained with different numbers of epochs and window sizes when predicting a single time point using true fMRI data. (b) The MSE of the models trained with 20 epochs and different window sizes when predicting long time series using the previous predicted time points (predicted brain states are used for future prediction).
Fig. 3. Time series prediction MSE and correlation between the predicted and true brain states. The red dashed line specifies the first prediction starting point, which is the MSE of the 51 st time points. The green dash line shows the point where the time points after it (on the right-hand side) are predicted purely depending on previously predicted data. Time points between two dashed lines are predicted partially based on real data. (a) The MSE of all predicted time points averaged across all test subjects. (b) The zoomed-in version of (a), showing the MSE of the first 100 predicted time points averaged across all test subjects. (c) The correlation coefficients of all 1150 predicted brain states averaged across all test subjects. (d) The zoomed-in version of (c), showing the correlation coefficients of the first 50 predicted brain states averaged across all test subjects. The red and green dash lines possess correspondence across the 4 sub-figures.
Fig. 4. FC matrices, mean absolute difference, and Pearson's correlation. (a) The group average FC calculated based on the true fMRI (b) The group average FC calculated based on predicted fMRI. (c) The FC calculated based on the predicted fMRI for one session of a subject. (d) The FC calculated based on the predicted fMRI for one session of another subject (e) Distribution of the mean absolute differences and Pearson's correlation coefficients between predicted and true group average FC. The mean absolute difference is shown on the left, and Pearson's correlation is shown on the right. The three horizontal lines specify the minimum, maximum, and mean of the mean absolute difference and Pearson's correlation, respectively.
Predicting Human Brain States with Transformer
  • Preprint
  • File available

December 2024

·

31 Reads

The human brain is a complex and highly dynamic system, and our current knowledge of its functional mechanism is still very limited. Fortunately, with functional magnetic resonance imaging (fMRI), we can observe blood oxygen level-dependent (BOLD) changes, reflecting neural activity, to infer brain states and dynamics. In this paper, we ask the question of whether the brain states rep-resented by the regional brain fMRI can be predicted. Due to the success of self-attention and the transformer architecture in sequential auto-regression problems (e.g., language modelling or music generation), we explore the possi-bility of the use of transformers to predict human brain resting states based on the large-scale high-quality fMRI data from the human connectome project (HCP). Current results have shown that our model can accurately predict the brain states up to 5.04s with the previous 21.6s. Furthermore, even though the prediction error accumulates for the prediction of a longer time period, the gen-erated fMRI brain states reflect the architecture of functional connectome. These promising initial results demonstrate the possibility of developing gen-erative models for fMRI data using self-attention that learns the functional or-ganization of the human brain. Our code is available at: https://github.com/syf0122/brain_state_pred.

Download

Summary of average and standard deviation values for the mean absolute error between enhanced DTI and ground truth of common DTI-derived scalar metrics. The units of measurement for AD, MD, and RD are expressed in mm 2 /s.
Enhancing Angular Resolution via Directionality Encoding and Geometric Constraints in Brain Diffusion Tensor Imaging

September 2024

·

32 Reads

Diffusion-weighted imaging (DWI) is a type of Magnetic Resonance Imaging (MRI) technique sensitised to the diffusivity of water molecules, offering the capability to inspect tissue microstructures and is the only in-vivo method to reconstruct white matter fiber tracts non-invasively. The DWI signal can be analysed with the diffusion tensor imaging (DTI) model to estimate the directionality of water diffusion within voxels. Several scalar metrics, including axial diffusivity (AD), mean diffusivity (MD), radial diffusivity (RD), and fractional anisotropy (FA), can be further derived from DTI to quantitatively summarise the microstructural integrity of brain tissue. These scalar metrics have played an important role in understanding the organisation and health of brain tissue at a microscopic level in clinical studies. However, reliable DTI metrics rely on DWI acquisitions with high gradient directions, which often go beyond the commonly used clinical protocols. To enhance the utility of clinically acquired DWI and save scanning time for robust DTI analysis, this work proposes DirGeo-DTI, a deep learning-based method to estimate reliable DTI metrics even from a set of DWIs acquired with the minimum theoretical number (6) of gradient directions. DirGeo-DTI leverages directional encoding and geometric constraints to facilitate the training process. Two public DWI datasets were used for evaluation, demonstrating the effectiveness of the proposed method. Extensive experimental results show that the proposed method achieves the best performance compared to existing DTI enhancement methods and potentially reveals further clinical insights with routine clinical DWI scans.


Fig. 1| Population template and data preparation analysis workflow. a, Multi-modal population 846 template consisting T1w, T2w, FA, and MD contrasts and FOD computed from the collective data from 50 847 subjects. The template was generated at 0.7 mm isotropic resolution as described in 39 . b, The 3D 848 volumetric maps (T1w, T2w, FA, MD, AD, and RD) from individual subjects were registered to the template 849 space using non-linear warping. 20M streamlines were generated in the subject space by dynamic seeding 850 and by seeding within subcortex, which were then warped to the template space. The SNR of the data 851 was further improved by computing track-Weighted Imaging (TWI) maps 40,41 for the aforementioned 852 contrasts (using the streamlines warped to the template) at 0.7 mm isotropic super-resolution. Direction 853 encoded colour (DEC) maps were computed from the subject specific FODs. The volumes corresponding 854 to the red (DEC-r), green (DEC-g), and blue (DEC-b) colour channels were extracted for directional 855 information of the FODs along the right-left, anterior-posterior, and superior-inferior direction, which 856 were then warped to template space at 0.7 mm isotropic resolution. 857
Fig. 2| Hierarchical bimodal clustering of caudate. a, Clustering tree diagram for caudate. Ten parametric 859 data set (FA, TW-T1w/T2w, TW-FA, TW-MD, TW-AD, TW-RD, TW-FODamp, DEC-r, DEC-b, DEC-g) 860 corresponding to the consensus caudate mask were isolated for 50 subjects and principal component 861 analysis (PCA) was performed on the 50x10 metrics. The principal components contributing to 65% or more 862 of the total variance were used for bi-modal k-means clustering (cluster 1 and 2). This process was repeated 863 until a cluster was found to be smaller than the known SGM sub-regions according to histology-based 864 atlas 11 . b, Caudate parcellation along the coronal, axial, and sagittal plane. c, Caudate parcels shown by 3D 865 rendering. Clusters 1, 2.1, 2.2.1, and 2.2.2 were denoted as the lateral caudate (CdL), tail of caudate (CdT), 866 ventral caudate (CdV), and medial caudate (CdM). 867
Fig. 3| Nuclei and sub-regions delineation by SydSGM Parcellation. a, Coronal, axial, and sagittal cross 870 sections showing nuclei and specialised sub-regions of human subcortex delineated by data driven SGM 871 parcellation. For each of the SGM structure, the same approach as that described in Fig. 2 was carried out, 872 resulting in a total of 54 newly defined sub-regions. b, Coronal, axial, and sagittal representation of full 873 caudate, nucleus accumbens, putamen, globus pallidus, and thalamus used for parcellation. The resulting 874 parcels of these SGM structures are shown for the left brain. Full names of the parcels, abbreviations used, 875 and the corresponding regions in human brain atlas 11 can be found in Fig. 4 and Extended Data Figure 8. 876 877
Fig. 6| Structural connectivity alterations in Parkinson's Disease detected using SydSGM Parcellation. a, 911 Results obtained by Network Based Statistics 46 (NBS) using standard DK 21 atlas for cortical and subcortical 912 parcellation. Adjacency matrix shows the nodes of the network found to have significantly (p = 0.05) lower 913 connectivity strength in an early Parkinson's disease cohort (N = 20) compared to controls (N = 20). One 914 subcortical and 5 cortical nodes were involved in the only network identified using the DK atlas. b,c, NBS 915 results obtained from the same cohorts, now using extended DK-ex atlas, which replaces the whole SGM 916 structures (in DK 21 atlas) by SydSGM parcellation (54 nodes were added representing nuclei and sub-regions 917 within caudate, nucleus accumbens, putamen, globus pallidus, and thalamus). With the increase in spatial 918 specificity, two separate and extensive networks were demonstrated to have significantly (p = 0.05) lower 919 connectivity strength in early Parkinson's disease. The first network encompassed all the nodes identified 920 by the standard DK atlas analysis above, but identified additional cortical and SGM nodes that were part of 921 the same abnormal network (b). The second network (c), which was undetectable using conventional 922 parcellation, consisted of 3 cortical nodes and 8 SGM nodes demonstrating the rigorous involvement of 923 SGM nuclei and sub-regions in early Parkinson's disease. 924 925
In-vivo parcellation of human subcortex by multi-modal MRI

April 2024

·

251 Reads

Human subcortical grey matter (SGM) structures are crucial hubs for processing, transmitting and modulating brain-wide signals. However, in-vivo characterisation of subnuclei within the SGM structures remains limited, restricting our knowledge of their roles in brain function and in disorders. To address this gap, we introduce the Sydney Subcortical Gray Matter (SydSGM) parcellation— based on a novel data-driven approach using multiple MRI contrasts to assess myelination, directionality of white matter fibres, and diffusion micro-environment. Fifty-four distinct parcels were identified across caudate, putamen, globus pallidus, nucleus accumbens, and thalamus based on the coherent representation of these attributes, which demonstrated remarkable concordance with histology-based delineations. The SydSGM parcellation now facilitates detailed structural connectivity analysis at the subnuclei scale. It can be adopted as a standalone atlas or incorporated into existing brain atlases. We demonstrate SydSGM parcellation’s advantage by revealing declined structural connectivity of highly resolved SGM subnuclei in patients with early Parkinson’s disease.



Example of a 18-year old patient with surgically confirmed FCDII of the right inferior triangular gyrus; MRI was considered abnormal by the senior radiologist. (A) Presurgical T1-weighted imaging: the white arrow shows focal thickening of the cerebral cortex at the level of the right inferior triangular gyrus. (B) Presurgical FLAIR sequence: the white arrow shows a subcortical region with FLAIR signal hyperintensity at the level of the dysplastic zone. (C) AssemblyNet segmentation of the right inferior triangular gyri, the yellow arrow shows the right sided region. (D) Postsurgical T1-weighted scan: postoperative cavity (white arrow).
Normative range for the % volume of four structures for the 47-year-old randomly selected patient with FCD in the left supplementary motor cortex (A, Subject 25). The variation between normative ranges depends on the anatomical area. For example, the LifeSpan-based normative values placed the patient’s left supplementary motor cortex in the upper end of the range but still within the normal range (green cross), while GeoNorm considered this structure outside of the personalized normative values (red cross). Both models also found the left superior parietal lobule as atrophic, but this was less severe using GeoNorm. The Presurgical T1-weighted imaging (B) and AssemblyNet segmentation (C) are shown in the bottom row. For example, for the left supplementary motor cortex, AssemblyNet classified it as normal, while the GeoNorm personalized normative values classified it as with increased volume.
Pipeline in the proposed GeoNorm framework. For each subject of interest (e.g. an epileptic patient), the K-nearest neighbors from the large healthy control group are identified (in this study, K = 30). The nearest controls, together with the subject of interest were moved to a reduced manifold subspace (which was computed from the 30 nearest controls). We considered that the residuals ε was representative of any abnormalities present in a new patient when it is greater than the model variability learned during a leave-one-out procedure on controls.
Data-driven normative values based on generative manifold learning for quantitative MRI

March 2024

·

60 Reads

·

3 Citations

In medicine, abnormalities in quantitative metrics such as the volume reduction of one brain region of an individual versus a control group are often provided as deviations from so-called normal values. These normative reference values are traditionally calculated based on the quantitative values from a control group, which can be adjusted for relevant clinical co-variables, such as age or sex. However, these average normative values do not take into account the globality of the available quantitative information. For example, quantitative analysis of T1-weighted magnetic resonance images based on anatomical structure segmentation frequently includes over 100 cerebral structures in the quantitative reports, and these tend to be analyzed separately. In this study, we propose a global approach to personalized normative values for each brain structure using an unsupervised Artificial Intelligence technique known as generative manifold learning. We test the potential benefit of these personalized normative values in comparison with the more traditional average normative values on a population of patients with drug-resistant epilepsy operated for focal cortical dysplasia, as well as on a supplementary healthy group and on patients with Alzheimer’s disease.


A novel method for PET connectomics guided by fibre-tracking MRI: Application to Alzheimer's disease

March 2024

·

42 Reads

·

1 Citation

This study introduces a novel brain connectome matrix, track‐weighted PET connectivity ( twPC ) matrix, which combines positron emission tomography ( PET ) and diffusion magnetic resonance imaging data to compute a PET ‐weighted connectome at the individual subject level. The new method is applied to characterise connectivity changes in the Alzheimer's disease (AD) continuum. The proposed twPC samples PET tracer uptake guided by the underlying white matter fibre‐tracking streamline point‐to‐point connectivity calculated from diffusion MRI ( dMRI ) . Using tau‐PET , dMRI and T1 ‐weighted MRI from the Alzheimer's Disease Neuroimaging Initiative database, structural connectivity ( SC ) and twPC matrices were computed and analysed using the network‐based statistic (NBS) technique to examine topological alterations in early mild cognitive impairment ( MCI ), late MCI and AD participants. Correlation analysis was also performed to explore the coupling between SC and twPC . The NBS analysis revealed progressive topological alterations in both SC and twPC as cognitive decline progressed along the continuum. Compared to healthy controls, networks with decreased SC were identified in late MCI and AD , and networks with increased twPC were identified in early MCI , late MCI and AD . The altered network topologies were mostly different between twPC and SC , although with several common edges largely involving the bilateral hippocampus, fusiform gyrus and entorhinal cortex. Negative correlations were observed between twPC and SC across all subject groups, although displaying an overall reduction in the strength of anti‐correlation with disease progression. twPC provides a new means for analysing subject‐specific PET and MRI ‐derived information within a hybrid connectome using established network analysis methods, providing valuable insights into the relationship between structural connections and molecular distributions. Practitioner Points New method is proposed to compute patient‐specific PET connectome guided by MRI fibre‐tracking. Track‐weighted PET connectivity (twPC) matrix allows to leverage PET and structural connectivity information. twPC was applied to dementia, to characterise the PET nework abnormalities in Alzheimer's disease and mild cognitive impairment.


2449 Investigating structural connectivity and cognition in multiple sclerosis (MS) over two years

February 2024

·

8 Reads

Objectives Structural connectivity and graph theory analysis may elucidate substrates for cognitive impairment in MS, providing additional biomarkers for disease progression in addition to conventional MRI analysis. We investigated the association between global network metrics and cognitive impairment cross-sectionally and longitudinally over 24 months. Methods 48 patients with relapsing-remitting MS were recruited for an observational study over 24 months. Processing speed, learning/memory, executive function and language were assessed with the Minimal Assessment of Cognitive Function in MS (MACFIMS) battery. Patients were divided into cognitively-impaired (Z score <-1SD for ≥ 2 tests) versus cognitively-preserved at baseline. Structural connectomes were reconstructed using diffusion-weighted imaging and probabilistic tractography.Network metrics and cognitive scores were analysed at baseline, and network metric changes were correlated with cognitive changes over 24 months. Results Mean age was 30 years, 63% female, median disease duration 1.88 years, median EDSS 2.0. At baseline, network metrics were not correlated with any cognitive outcomes after adjusting for age, gender and T2 lesion volume. Between cognitively impaired (n=25) and preserved patients (n=23), there was no difference in lesion volume, normalised gray matter volume, or network metrics. Baseline EDSS was not significantly correlated with any graph measures. Over two years, change in executive function was negatively correlated with change in mean local efficiency, clustering co- efficient and assortativity (p<0.05). Conclusion Global network metrics were not correlated with cognitive outcomes or EDSS scores, and did not differentiate between cognitively impaired vs preserved patients. Longitudinal changes in global network metrics are associated with cognitive decline.


Trajectories of white matter degeneration in frontotemporal dementia: new insights using fixel‐based analysis

December 2023

·

50 Reads

Background The three clinical presentations of frontotemporal dementia (FTD), behavioral‐variant FTD (bvFTD), progressive non‐fluent aphasia (PNFA), and semantic dementia (SD), are caused by abnormal aggregation of protein pathologies and progressive brain atrophy that precede the emergence of clinical symptoms. Neuropathology shows a ‘prion‐like’ propagation along white matter pathways before aggregating in the neuronal cell bodies of the grey matter. The in‐vivo mapping of upstream white matter trajectories of degeneration can improve disease staging and understanding of disease mechanisms in FTD, but longitudinal white matter studies are rare due to methodological challenges. Here, we develop a novel neuroimaging approach to map white matter changes in FTD using fixel‐based analysis (FBA), a new diffusion weighted imaging technique. Method Sixty FTD patients (23 bvFTD, 15 PNFA and 22 SD) were matched with 30 heathy controls. Participants underwent a comprehensive annual clinical assessment and high‐resolution multimodal MRI (median 2 years; range 1‐6 years). Time‐varying changes in white matter fibre density and cross‐section were computed with MRtrix3, FreeSurfer 7.1.1 and Spatiotemporal Linear Mixed Effects models. Result All FTD syndromes showed more extensive white matter changes at baseline than previously reported. In bvFTD, atrophy extended posteriorly over time, encroaching in tracts connecting subcortical and motor‐associations regions (Fig 1). In PNFA, baseline left‐lateralized atrophy extended anteriorly and inferiorly and into the contralateral hemisphere, eventually mirroring patterns of atrophy at presentation (Fig 2). In SD, the focal left temporal baseline atrophy extended posteriorly and laterally along the inferior and superior longitudinal fasculi, and involving the right hemisphere with disease progression (Fig 3). Conclusion The FBA approach enabled the identification of new white matter bundles in FTD, with syndrome‐specific effects and improved biological interpretability compared to traditional tensor imaging methods. Our method can be extended beyond FTD to other neurodegenerative disorders to inform better models of disease staging and provide useful targets for patient stratification and monitoring in trials of disease‐monitoring interventions.


High angular diffusion tensor imaging estimation from minimal evenly distributed diffusion gradient directions

September 2023

·

74 Reads

·

7 Citations

Frontiers in Radiology

Diffusion-weighted Imaging (DWI) is a non-invasive imaging technique based on Magnetic Resonance Imaging (MRI) principles to measure water diffusivity and reveal details of the underlying brain micro-structure. By fitting a tensor model to quantify the directionality of water diffusion a Diffusion Tensor Image (DTI) can be derived and scalar measures, such as fractional anisotropy (FA), can then be estimated from the DTI to summarise quantitative microstructural information for clinical studies. In particular, FA has been shown to be a useful research metric to identify tissue abnormalities in neurological disease (e.g. decreased anisotropy as a proxy for tissue damage). However, time constraints in clinical practice lead to low angular resolution diffusion imaging (LARDI) acquisitions that can cause inaccurate FA value estimates when compared to those generated from high angular resolution diffusion imaging (HARDI) acquisitions. In this work, we propose High Angular DTI Estimation Network (HADTI-Net) to estimate an enhanced DTI model from LARDI with a set of minimal and evenly distributed diffusion gradient directions. Extensive experiments have been conducted to show the reliability and generalisation of HADTI-Net to generate high angular DTI estimation from any minimal evenly distributed diffusion gradient directions and to explore the feasibility of applying a data-driven method for this task. The code repository of this work and other related works can be found at https://mri-synthesis.github.io/ .


Citations (59)


... The convolutional and pooling layers gather the high-level features to upsample them to the resolution of the initial image using a specific type of tied unpooling and convolutional layers. In [35] explore federated learning to enhance MS lesion segmentation in MRI, addressing data privacy and label noise issues. Introducing DHLC and CELC, our methods improve annotation accuracy and model robustness. ...

Reference:

Compound attention embedded dual channel encoder-decoder for ms lesion segmentation from brain MRI
Improving multiple sclerosis lesion segmentation across clinical sites: A federated learning approach with noise-resilient training
  • Citing Article
  • April 2024

Artificial Intelligence in Medicine

... The selection of similar patients may be distance-based according to a patient-similarity metric. This may imply a digital representation of patients as data points in a latent reduced multidimensional space, using linear or nonlinear dimension-reduction algorithms [60,61]. On the other hand, the selection of similar patients may be filter-based. ...

Data-driven normative values based on generative manifold learning for quantitative MRI

... This approach utilizes limited dMRI volumes to generate reliable diffusion metrics comparable to those derived from sufficient dMRI volumes. The feasibility of angular resolution enhancement has been explored in recent studies [17,3,5]. In the training process, the generator G A accepts artifact-free b 0 image concatenated with 6 unique DWIs (b 0 ⊕ 6DW Is) of a subject as inputs X ∈ R N ×7×W ×H×D and transforms X into an angular resolution enhanced FA map, denoted as F A ∈ R N ×1×W ×H×D . ...

High angular diffusion tensor imaging estimation from minimal evenly distributed diffusion gradient directions

Frontiers in Radiology

... BrEd in FSL is a visualization tool developed for inspecting NIfTI images with different Lightwave buttons for image manipulation like zoom, window, and sending to the overlay manager. [49][50][51] FSL and Python libraries also interface with Python. This allows execution of FSL on a map with a script without opening the FSL graphical user interface for projects. ...

The synergy of structural and functional connectivity
  • Citing Chapter
  • January 2023

... The segmentation findings reached a DSC=72%, outperforming the performance of U-net alone and U-net with the normal attention unit. Comparing the results with another study [47] that used the same public dataset including also data from three different hospitals, achieved a DSC=62%. ...

Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning

... The copyright holder for this this version posted December 11, 2024. ; https://doi.org/10.1101/2024.12.06.24318287 doi: medRxiv preprint 2019; Kapadia et al., 2020;Purrer et al., 2021;Kyle et al., 2023). Similarly, we observed that after one month overall lesion volume marginally correlated with concurrent improvement in tremor severity, but it was not associated with tremor change after six months. ...

Tremor suppression following treatment with MRgFUS: skull density ratio consistency and degree of posterior dentatorubrothalamic tract lesioning predicts long-term clinical outcomes in essential tremor

... 42 To date, most neuroimaging studies have used group-based analyses that are unable to account for the significant individual differences in mTBI presentations. 43 Applying the concept of clinical profiles or subtypes to MRI analysis may provide more meaningful insight relating microstructural injury in brain regions specific to an individual's clinical presentation. 20,42,44 Improving our understanding of the cause of post-mTBI symptoms provides valuable insight into possible management approaches. ...

Individualised Profiling of White Matter Organisation in Moderate-to-Severe Traumatic Brain Injury Patients
  • Citing Article
  • February 2023

Brain Research

... Consisting of more than 50% of the total neurons in the whole brain ( Wagner et al., 2019;Zagon et al., 1977), the cerebellum has a repeated, modular structure that integrates the cerebellar cortex with the cerebral cortex, both via inputs (via the cortico-pontine-cerebellar system) and outputs (via the cerebello-thalamo-cortical circuits) ( Albus, 1971;Koziol et al., 2014;Nashef et al., 2018), as well as with the spinal cord ( Albus, 1971;Cohen et al., 2017;Zinger et al., 2013). The modular structure of the cerebellum allows for compartmentalisation of unique inputs ( Hayter et al., 2007;Kostadinov & Häusser, 2022), the learning and execution of complex, nested sequences of input-output patterns ( Khilkevich et al., 2018), as well as more complex, multidomain functions like parallel processing ( Müller et al., 2023). Furthermore, the cerebellum has also been linked to pattern separation and feed-forward predictions, akin to a forward model for effective motor control ( Bastian, 2006;Imamizu & Kawato, 2009;Ramnani, 2014;Stein, 2021;Wolpert et al., 1998). ...

Parallel processing relies on a distributed, low-dimensional cortico-cerebellar architecture

... Two groups used two different divisions of the three subsets, using the first one to determine the best method, and the Table 1 Employed AI-reconstruction methods to synthesize FA, AD and MD from 21 gradient directions. FCN: Fully Connected Network; CNN: Convolutional neural network; MLP: Multilayer perceptron; UN: Unrolled network (Ye et al., 2020;HashemizadehKolowri et al., 2022); U-Net (Çiçek et al., 2016;Tang et al., 2021, Tang et al., 2022; DAE: Denoising autoencoder (Faiyaz et al., 2022b); AEME: Adaptive network with extragradient for dMRI based microstructure estimation (Zheng et al., 2022); SARDI-Net: Super-angular Resolution Diffusion Imaging Network ; DNSR: Dual network scoring and reconstruction (Blumberg et al., 2022); Swin-Transformer . The term enhanced metrics used in the description of the methods refers to the 3 considered metrics (FA, MA, AD) calculated by the different methods to achieve a quality similar to the parameters estimated from 61 gradient directions. ...

Diffusion MRI Fibre Orientation Distribution Inpainting
  • Citing Chapter
  • December 2022

Lecture Notes in Computer Science

... Decreased functional connectivity in the DMN has been reported in many studies . T his pattern of DMN dysfunction has been displayed in the MCI group with limited increases compared with the CN group between DMN structures, indicative of a pr odr omal compensatory mec hanism (DeMayo et al., 2023a ;Niu et al., 2018 ;Zhang et al., 2020 ;Zheng et al., 2017 ). Brain structures implicated in the DMN mostly include the posterior cingulate cortex (PCC), pr ecuneus cortex, medial pr efr ontal and later al tempor al cortices and hippocampus (Forouzannezhad et al., 2019 ). ...

Hippocampal Neuronal Integrity and Functional Connectivity Within the Default Mode Network in Mild Cognitive Impairment: A Multimodal Investigation
  • Citing Article
  • November 2022

Brain Connectivity