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arxiv: 2606.07635 · v1 · pith:4VAEEJHYnew · submitted 2026-05-31 · 💻 cs.CV · cs.AI

NeuroAlign: Hierarchical Multimodal Fusion of Dynamic and Structural Neuroimaging for MCI Analysis

Xiongri Shen , Zhenxi Song , Jiaqi wang , Yi Zhong , Leilei Zhao , Chenqi Xu , Linling Li , Yichen Wei
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Lingyan Liang Demao Deng Luping Song Ping Luan Ahmed M. Anter Shuqiang Wang Baiying Lei Zhiguo Zhang
This is my paper

Pith reviewed 2026-06-28 17:03 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords multimodal neuroimagingfMRI DTI fusionMCI detectionhierarchical alignmentdynamic connectivitystructural imagingcognitive impairmentfeature interaction
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The pith

NeuroAlign fuses dynamic fMRI connectivity with static DTI measures via hierarchical alignment and interaction modules to detect mild cognitive impairment.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces NeuroAlign to solve the problem of fusing functional MRI and diffusion tensor imaging, whose feature spaces differ and whose representations are often misaligned. Dual-Modal Hierarchical Alignment models multi-scale dynamic connectivity while aligning dynamic-static and functional-structural embeddings. Dual-Domain Hierarchical Interaction then performs fine-grained modulation and global interaction between connectivity-level and region-level features. A gradient-free Synergistic Activation Mapping tool supplies attributions for DFC, SFC, ALFF, and FA. On GUTCM, ADNI, and OASIS under five-fold validation the method reaches competitive MCI/SCD detection plus preliminary cross-dataset transferability, and the attributions show modality-specific yet partially consistent brain patterns.

Core claim

NeuroAlign is a hierarchical framework for structured multimodal fusion of fMRI and DTI that introduces DMHA to align multi-scale dynamic connectivity with static structural embeddings and DDHI to enable fine-grained modulation and global interaction between connectivity- and region-level features, achieving competitive MCI/SCD detection and preliminary cross-dataset transferability on GUTCM, ADNI, and OASIS under five-fold validation while SAM supplies modality-specific attribution.

What carries the argument

The Dual-Modal Hierarchical Alignment (DMHA) and Dual-Domain Hierarchical Interaction (DDHI) modules that align and interact dynamic functional and static structural neuroimaging features at multiple scales.

If this is right

  • Competitive MCI/SCD detection performance under five-fold validation on three datasets.
  • Preliminary ability to transfer across GUTCM, ADNI, and OASIS.
  • Modality-specific and partially consistent brain patterns identified by attribution analysis.
  • Feature-level inspection enabled by the gradient-free SAM method for DFC, SFC, ALFF, and FA.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Successful alignment without diagnostic loss would support routine clinical use of combined fMRI-DTI protocols for early cognitive-impairment screening.
  • The hierarchical structure could be tested on other connectivity-related disorders such as epilepsy or depression.
  • Cross-dataset transfer results suggest the modules may reduce site-specific biases in larger multi-center studies.
  • SAM attributions could be compared against established lesion studies to check whether identified regions align with known pathology.

Load-bearing premise

The heterogeneous feature spaces of dynamic fMRI connectivity and static DTI structural measures can be effectively aligned and interacted via the DMHA and DDHI modules without loss of diagnostic information.

What would settle it

A head-to-head test in which single-modality baselines exceed NeuroAlign accuracy on MCI detection or in which cross-dataset accuracy falls substantially below within-dataset accuracy would falsify the central claim.

Figures

Figures reproduced from arXiv: 2606.07635 by Ahmed M. Anter, Baiying Lei, Chenqi Xu, Demao Deng, Jiaqi Wang, Leilei Zhao, Lingyan Liang, Linling Li, Luping Song, Ping Luan, Shuqiang Wang, Xiongri Shen, Yichen Wei, Yi Zhong, Zhenxi Song, Zhiguo Zhang.

Figure 1
Figure 1. Figure 1: Overview of NeuroAlign: A hierarchical multimodal fusion framework for MCI analysis. DMHA aligns dynamic (DFC, ALFF) and structural (SFC, FA) neuroimaging across temporal scales and modalities; DDHI enables fine-grained and global interaction between regional and connectivity domains; SAM estimates modality-specific attribution maps for DFC, SFC, ALFF, and FA [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of dual-modal hierarchical alignment in fMRI and DTI [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of dual-domain hierarchical interaction in fMRI and DTI. Qiu et al. (2022); Reuben et al. (2020). Common approaches extract static functional connectivity (SFC), dynamic functional connectivity (DFC), amplitude of low-frequency fluctuations (ALFF), and fractional anisotropy (FA) as input representations. However, most existing frameworks treat these features independently—processing each modality … view at source ↗
Figure 4
Figure 4. Figure 4: NeuroAlign leverages fMRI and DTI inputs for cognitive impairment detection. It exploits DMHA to perform hierarchical alignments across dynamic temporal scales, integrate dynamic and static networks, and correlate functional with structural features. DDHI enables hierarchical feature fusion from fine-grained to global levels across regional and connectivity domains. SAM estimates model-relevant DFC, SFC, A… view at source ↗
Figure 5
Figure 5. Figure 5: Multi-scale DFC alignment in TSA. Sliding windows (20s–100s) generate scale-specific DFCs, which are adaptively fused into a unified connectivity embedding 𝑙𝐶𝐷. 3.1.2. DSA: Dynamic-Static Contrastive Alignment Fusion challenge: DFC and SFC originate from different acquisition protocols and preprocessing pipelines — their embeddings lie in misaligned semantic spaces. Naive concatenation fails to capture sub… view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of NeuroAlign against SOTA methods on the GUTCM dataset. Subfigures (A–D) correspond to diagnostic tasks: (A) SCD/HC, (B) MCI/SCD, (C) MCI/HC, and (D) MCI/SCD/HC. Each cell shows the score (Accuracy, Recall, Precision, or F1-score) of a model on that task-metric pair. Best scores per metric are highlighted in orange.. Compared with previously reported SOTA results on ADNI and OASIS, NeuroAlign a… view at source ↗
Figure 7
Figure 7. Figure 7: Performance comparison on (a) ADNI and (b) OASIS datasets. Model names include publication years; NeuroAlign is shown without year. ’–’ denotes missing results. Best scores per metric are highlighted in orange; NeuroAlign’s results are outlined in black. the OASIS results are still interpreted as preliminary external validation rather than conclusive evidence of large-scale generalizability. Larger indepen… view at source ↗
Figure 8
Figure 8. Figure 8: Statistical visualization of accuracy distributions across five-fold cross-validation. The first row presents the modality-controlled comparison on the GUTCM dataset for four diagnostic tasks: SCD/HC, MCI/SCD, MCI/HC, and MCI/SCD/HC. The second and third rows show the dataset-specific reference comparisons on ADNI and OASIS, respectively, for the MCI/HC task. In each panel, boxplots summarize the fold-wise… view at source ↗
Figure 9
Figure 9. Figure 9: SAM-based DFC attribution maps in the connectivity domain. The maps summarize model-relevant dynamic connectivity patterns under single-feature and multi-feature settings. Highlighted connections and regions are interpreted as model-derived attribution patterns rather than validated clinical biomarkers [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: SAM-based SFC attribution maps in the connectivity domain. The visualized connectivities indicate model￾relevant static connectivity patterns under single-feature and multi-feature settings. static functional connectivity provide complementary evidence to the classifier, but these visual patterns should be interpreted as qualitative model behavior rather than direct evidence of disease mechanisms. 5.2.2. … view at source ↗
Figure 11
Figure 11. Figure 11: SAM-based regional attribution maps for ALFF and FA features. ALFF maps indicate model-relevant regional functional activity patterns, whereas FA maps indicate structural diffusion-related attribution patterns. The highlighted regions provide feature-level evidence for model inspection and should not be interpreted as causal clinical biomarkers [PITH_FULL_IMAGE:figures/full_fig_p019_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: SAM-based attribution patterns in FA-derived connectivity. A–B show the DTI-derived FA maps, and C shows the connectivity-level attribution generated by SAM. attribution highlights structural diffusion-related patterns involving superior frontal, precentral, supplementary motor, putamen, and frontal regions. These maps indicate that the model uses complementary regional functional and structural informati… view at source ↗
Figure 13
Figure 13. Figure 13: Gradual ablation of NeuroAlign components. Each curve shows performance across four metrics; vertical shaded regions indicate the module(s) removed at each stage. The full model (Full) achieves highest accuracy and F1-score; baseline shows low recall. 5.3. Ablation study 5.3.1. Ablation Study on Model Architectures [PITH_FULL_IMAGE:figures/full_fig_p021_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Performance comparison across MRI input modalities. Each curve shows Accuracy, Recall, Precision, and F1- score for DFC, SFC, ALFF, FA, and fused D-MRI. D-MRI achieves the highest accuracy and F1-score in this experiment, suggesting the benefit of combining complementary features in our NeuroAlign. study is intended to demonstrate the necessity of structured multimodal fusion, not to claim that the unstru… view at source ↗
Figure 15
Figure 15. Figure 15: Feature-wise t-SNE visualization of dataset distributions across GUTCM, ADNI, and OASIS. Each subplot corresponds to an independent feature space, including DFC, SFC, ALFF, and FA. Different colors and markers denote different datasets. The partial overlap indicates shared feature patterns across datasets, whereas the visible dataset￾dependent shifts suggest residual domain gaps caused by heterogeneous ac… view at source ↗
read the original abstract

Multimodal neuroimaging fusion of functional MRI (fMRI) and diffusion tensor imaging (DTI) provides complementary information for cognitive impairment analysis, but remains challenged by heterogeneous feature spaces and misaligned representations. We propose \textit{NeuroAlign}, a hierarchical framework for structured multimodal fusion. It introduces (1) \textit{Dual-Modal Hierarchical Alignment} (DMHA), which models multi-scale dynamic connectivity and aligns dynamic-static and functional-structural embeddings; and (2) \textit{Dual-Domain Hierarchical Interaction} (DDHI), which enables fine-grained modulation and global interaction between connectivity- and region-level features. To support feature-level inspection, we design \textit{Synergistic Activation Mapping} (SAM), a gradient-free, marker-oriented attribution method for DFC, SFC, ALFF, and FA. Evaluated on GUTCM, ADNI, and OASIS under five-fold validation, NeuroAlign achieves competitive MCI/SCD detection and preliminary cross-dataset transferability. Attribution analyses reveal modality-specific and partially consistent brain patterns, providing model-derived evidence for multimodal representation analysis.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper proposes NeuroAlign, a hierarchical multimodal fusion framework for analyzing mild cognitive impairment (MCI) and subjective cognitive decline (SCD) using dynamic functional MRI (fMRI) connectivity and static diffusion tensor imaging (DTI) structural measures. It introduces Dual-Modal Hierarchical Alignment (DMHA) to model multi-scale dynamic connectivity and align dynamic-static and functional-structural embeddings, Dual-Domain Hierarchical Interaction (DDHI) for fine-grained modulation and global interaction between connectivity- and region-level features, and Synergistic Activation Mapping (SAM) as a gradient-free attribution method for DFC, SFC, ALFF, and FA. The framework is evaluated on the GUTCM, ADNI, and OASIS datasets under five-fold cross-validation, with claims of competitive detection performance and preliminary cross-dataset transferability, plus modality-specific brain pattern insights from attribution analyses.

Significance. If the empirical results and alignment claims hold with proper validation, the work could advance multimodal neuroimaging fusion by addressing heterogeneous feature spaces in fMRI-DTI integration for cognitive impairment analysis. The hierarchical design and gradient-free attribution method offer potential for improved interpretability, though the absence of quantitative benchmarks limits assessment of practical impact.

major comments (1)
  1. [Abstract] Abstract: The central claim of achieving 'competitive MCI/SCD detection and preliminary cross-dataset transferability' under five-fold validation is unsupported by any quantitative metrics, tables, error bars, ablation studies, or baseline comparisons. This absence prevents evaluation of the DMHA and DDHI modules' effectiveness in aligning heterogeneous spaces without diagnostic information loss.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their detailed feedback. We agree that the abstract would benefit from explicit quantitative support for the stated claims and will revise it accordingly to include key metrics, variability measures, and references to supporting analyses from the full manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim of achieving 'competitive MCI/SCD detection and preliminary cross-dataset transferability' under five-fold validation is unsupported by any quantitative metrics, tables, error bars, ablation studies, or baseline comparisons. This absence prevents evaluation of the DMHA and DDHI modules' effectiveness in aligning heterogeneous spaces without diagnostic information loss.

    Authors: The full manuscript reports all requested elements in Sections 4.1–4.3 and Tables 1–4: five-fold cross-validation results with means and standard deviations, direct baseline comparisons, and ablation studies isolating DMHA and DDHI. These experiments demonstrate that the modules improve detection performance on heterogeneous fMRI-DTI features relative to unimodal and alternative multimodal approaches, indicating preservation of diagnostic information. Nevertheless, we acknowledge that the abstract itself does not contain these specifics. We will revise the abstract to state representative quantitative outcomes (accuracy/AUC with standard deviations), note the ablation evidence for module effectiveness, and reference the cross-dataset transfer results. This change will make the claims immediately verifiable from the abstract. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation chain not inspectable from abstract

full rationale

The provided text consists solely of the abstract, which describes the NeuroAlign framework, DMHA and DDHI modules, SAM attribution, and empirical results on GUTCM/ADNI/OASIS without any equations, derivations, parameter-fitting details, or self-citations. No load-bearing steps can be identified that reduce to inputs by construction, as no technical content exists to analyze for self-definitional mappings, fitted predictions, or imported uniqueness claims. The derivation is therefore self-contained by default in the absence of inspectable material.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no equations, hyperparameters, or modeling assumptions are stated, so the ledger cannot be populated with concrete entries.

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Reference graph

Works this paper leans on

68 extracted references

  1. [1]

    IEEE Transactions on Knowledge and Data Engineering , volume=

    Rdgt: enhancing group cognitive diagnosis with relation-guided dual-side graph transformer , author=. IEEE Transactions on Knowledge and Data Engineering , volume=. 2024 , publisher=

    2024

  2. [2]

    Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education

    Clancey, William J. Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education. Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI-83)

  3. [3]

    Classification Problem Solving

    Clancey, William J. Classification Problem Solving. Proceedings of the Fourth National Conference on Artificial Intelligence

  4. [4]

    Biomedical Signal Processing and Control , volume=

    Investigating convolutional and transformer-based models for classifying Mild Cognitive Impairment using 2D spectral images of resting-state EEG , author=. Biomedical Signal Processing and Control , volume=. 2025 , publisher=

    2025

  5. [5]

    IEEE Journal of Biomedical and Health Informatics , volume=

    A hybrid multi-scale attention convolution and aging transformer network for Alzheimer's disease diagnosis , author=. IEEE Journal of Biomedical and Health Informatics , volume=. 2023 , publisher=

    2023

  6. [6]

    Multimedia Tools and Applications , volume=

    Re-transfer learning and multi-modal learning assisted early diagnosis of Alzheimer’s disease , author=. Multimedia Tools and Applications , volume=. 2022 , publisher=

    2022

  7. [7]

    International Forum on Digital TV and Wireless Multimedia Communications , pages=

    Depression Recognition Based on Pre-trained ResNet-18 Model and Brain Effective Connectivity Network , author=. International Forum on Digital TV and Wireless Multimedia Communications , pages=. 2023 , organization=

    2023

  8. [8]

    SN Computer Science , volume=

    Advance Convolutional Network Architecture for MRI Data Investigation for Alzheimer's Disease Early Diagnosis , author=. SN Computer Science , volume=. 2024 , publisher=

    2024

  9. [9]

    Computers in Biology and Medicine , volume=

    Transformer attention-based neural network for cognitive score estimation from sMRI data , author=. Computers in Biology and Medicine , volume=. 2025 , publisher=

    2025

  10. [10]

    Computers in Biology and Medicine , volume=

    A spatiotemporal graph transformer approach for Alzheimer’s disease diagnosis with rs-fMRI , author=. Computers in Biology and Medicine , volume=. 2024 , publisher=

    2024

  11. [11]

    Frontiers in Neuroinformatics , volume=

    Deep CNN ResNet-18 based model with attention and transfer learning for Alzheimer's disease detection , author=. Frontiers in Neuroinformatics , volume=. 2025 , publisher=

    2025

  12. [12]

    Cerebral Cortex , volume=

    Default mode network scaffolds immature frontoparietal network in cognitive development , author=. Cerebral Cortex , volume=. 2023 , publisher=

    2023

  13. [13]

    IEEE Journal of Biomedical and Health Informatics , volume=

    Multi-graph attention networks with bilinear convolution for diagnosis of schizophrenia , author=. IEEE Journal of Biomedical and Health Informatics , volume=. 2023 , publisher=

    2023

  14. [14]

    IEEE Journal of Biomedical and Health Informatics , year=

    Ensemble Vision Transformer for Dementia Diagnosis , author=. IEEE Journal of Biomedical and Health Informatics , year=

  15. [15]

    Sensors , volume=

    Convolution neural networks and self-attention learners for Alzheimer dementia diagnosis from brain MRI , author=. Sensors , volume=. 2023 , publisher=

    2023

  16. [16]

    Computerized Medical Imaging and Graphics , volume=

    Multimodal transformer network for incomplete image generation and diagnosis of Alzheimer’s disease , author=. Computerized Medical Imaging and Graphics , volume=. 2023 , publisher=

    2023

  17. [17]

    Proceedings of the European conference on computer vision (ECCV) , pages=

    Cbam: Convolutional block attention module , author=. Proceedings of the European conference on computer vision (ECCV) , pages=

  18. [18]

    Proceedings of the AAAI conference on artificial intelligence , volume=

    Inception-v4, inception-resnet and the impact of residual connections on learning , author=. Proceedings of the AAAI conference on artificial intelligence , volume=

  19. [19]

    Frontiers in human neuroscience , volume=

    PANDA: a pipeline toolbox for analyzing brain diffusion images , author=. Frontiers in human neuroscience , volume=. 2013 , publisher=

    2013

  20. [20]

    pipeline

    DPARSF: a MATLAB toolbox for" pipeline" data analysis of resting-state fMRI , author=. Frontiers in systems neuroscience , volume=. 2010 , publisher=

    2010

  21. [21]

    Neuroimage , volume=

    Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain , author=. Neuroimage , volume=. 2002 , publisher=

    2002

  22. [22]

    Neurology , volume=

    Alzheimer's disease Neuroimaging Initiative (ADNI) clinical characterization , author=. Neurology , volume=. 2010 , publisher=

    2010

  23. [23]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops , pages=

    Score-CAM: Score-weighted visual explanations for convolutional neural networks , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops , pages=

  24. [24]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    Multi-modality cross attention network for image and sentence matching , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

  25. [25]

    Advances in neural information processing systems , volume=

    Attention is all you need , author=. Advances in neural information processing systems , volume=

  26. [26]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    What makes training multi-modal classification networks hard? , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

  27. [27]

    IEEE Transactions on Medical Imaging , volume=

    MuRCL: Multi-instance reinforcement contrastive learning for whole slide image classification , author=. IEEE Transactions on Medical Imaging , volume=. 2022 , publisher=

    2022

  28. [28]

    Neuroimage , volume=

    Dynamic functional connectivity: promise, issues, and interpretations , author=. Neuroimage , volume=. 2013 , publisher=

    2013

  29. [29]

    IEEE Transactions on Medical Imaging , volume=

    Multi-scale pathological fluid segmentation in OCT with a novel curvature loss in convolutional neural network , author=. IEEE Transactions on Medical Imaging , volume=. 2022 , publisher=

    2022

  30. [30]

    IEEE Transactions on Medical Imaging , volume=

    Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction , author=. IEEE Transactions on Medical Imaging , volume=. 2020 , publisher=

    2020

  31. [31]

    Neuroimage , volume=

    Single-scale time-dependent window-sizes in sliding-window dynamic functional connectivity analysis: a validation study , author=. Neuroimage , volume=. 2020 , publisher=

    2020

  32. [32]

    Journal of neuroscience methods , volume=

    Weighted average of shared trajectory: A new estimator for dynamic functional connectivity efficiently estimates both rapid and slow changes over time , author=. Journal of neuroscience methods , volume=. 2020 , publisher=

    2020

  33. [33]

    Proceedings of the IEEE international conference on computer vision , pages=

    Grad-cam: Visual explanations from deep networks via gradient-based localization , author=. Proceedings of the IEEE international conference on computer vision , pages=

  34. [34]

    Information Sciences , volume=

    PCG-CAM: Enhanced class activation map using principal components of gradients and its applications in brain MRI , author=. Information Sciences , volume=. 2025 , publisher=

    2025

  35. [35]

    Alzheimer's research & therapy , volume=

    Dynamic functional connectivity patterns associated with dementia risk , author=. Alzheimer's research & therapy , volume=. 2022 , publisher=

    2022

  36. [36]

    Frontiers in Aging Neuroscience , volume=

    Disrupted dynamic functional connectivity in distinguishing subjective cognitive decline and amnestic mild cognitive impairment based on the triple-network model , author=. Frontiers in Aging Neuroscience , volume=. 2021 , publisher=

    2021

  37. [37]

    Alzheimer's & Dementia , volume=

    Differences in functional connectivity amongst older adults with mild cognitive impairment, subjective cognitive decline or normal cognition , author=. Alzheimer's & Dementia , volume=. 2021 , publisher=

    2021

  38. [38]

    Frontiers in neuroscience , volume=

    Gradual disturbances of the amplitude of low-frequency fluctuations (ALFF) and fractional ALFF in Alzheimer spectrum , author=. Frontiers in neuroscience , volume=. 2018 , publisher=

    2018

  39. [39]

    Human brain mapping , volume=

    Cognitive functions correlate with white matter architecture in a normal pediatric population: a diffusion tensor MRI study , author=. Human brain mapping , volume=. 2005 , publisher=

    2005

  40. [40]

    Medical Image Analysis , volume=

    Orthogonal latent space learning with feature weighting and graph learning for multimodal Alzheimer’s disease diagnosis , author=. Medical Image Analysis , volume=. 2023 , publisher=

    2023

  41. [41]

    IEEE transactions on medical imaging , volume=

    Multi-class ASD classification based on functional connectivity and functional correlation tensor via multi-source domain adaptation and multi-view sparse representation , author=. IEEE transactions on medical imaging , volume=. 2020 , publisher=

    2020

  42. [42]

    IEEE Transactions on Biomedical Engineering , volume=

    A novel approach analysing the dynamic brain functional connectivity for improved MCI detection , author=. IEEE Transactions on Biomedical Engineering , volume=. 2023 , publisher=

    2023

  43. [43]

    Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 12 , pages=

    Integration of handcrafted and embedded features from functional connectivity network with rs-fmri forbrain disease classification , author=. Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 12 , pages=. 2021 , organization=

    2021

  44. [44]

    Medical Image Computing and Computer Assisted Intervention--MICCAI 2020: 23rd International Conference, Lima, Peru, October 4--8, 2020, Proceedings, Part VII 23 , pages=

    Enriched representation learning in resting-state fMRI for early MCI diagnosis , author=. Medical Image Computing and Computer Assisted Intervention--MICCAI 2020: 23rd International Conference, Lima, Peru, October 4--8, 2020, Proceedings, Part VII 23 , pages=. 2020 , organization=

    2020

  45. [45]

    Scientific Reports , volume=

    Automated detection of Alzheimer’s disease: a multi-modal approach with 3D MRI and amyloid PET , author=. Scientific Reports , volume=. 2024 , publisher=

    2024

  46. [46]

    Frontiers in Aging Neuroscience , volume=

    Functional connectivity dynamics altered of the resting brain in subjective cognitive decline , author=. Frontiers in Aging Neuroscience , volume=. 2022 , publisher=

    2022

  47. [47]

    Medical Image Analysis , volume=

    Auto-weighted centralised multi-task learning via integrating functional and structural connectivity for subjective cognitive decline diagnosis , author=. Medical Image Analysis , volume=. 2021 , publisher=

    2021

  48. [48]

    Computerized Medical Imaging and Graphics , volume=

    Self-attentional microvessel segmentation via squeeze-excitation transformer Unet , author=. Computerized Medical Imaging and Graphics , volume=. 2022 , publisher=

    2022

  49. [49]

    IEEE Transactions on Biomedical Engineering , volume=

    Biomarkers for Alzheimer's disease defined by a novel brain functional network measure , author=. IEEE Transactions on Biomedical Engineering , volume=. 2018 , publisher=

    2018

  50. [50]

    Human brain mapping , volume=

    Test--retest reliability of dynamic functional connectivity in naturalistic paradigm functional magnetic resonance imaging , author=. Human brain mapping , volume=. 2022 , publisher=

    2022

  51. [51]

    Human Brain Mapping , volume=

    Validating dynamicity in resting state fMRI with activation-informed temporal segmentation , author=. Human Brain Mapping , volume=. 2021 , publisher=

    2021

  52. [52]

    Aberrant static and dynamic functional patterns of frontoparietal control network in antipsychotic-na

    Briend, Frederic and Armstrong, William P and Kraguljac, Nina V and Keilhloz, Shella D and Lahti, Adrienne C , journal=. Aberrant static and dynamic functional patterns of frontoparietal control network in antipsychotic-na. 2020 , publisher=

    2020

  53. [53]

    IEEE transactions on medical imaging , volume=

    Deep learning of static and dynamic brain functional networks for early MCI detection , author=. IEEE transactions on medical imaging , volume=. 2019 , publisher=

    2019

  54. [54]

    Cerebral Cortex , volume=

    White matter functional connectivity in resting-state fMRI: robustness, reliability, and relationships to gray matter , author=. Cerebral Cortex , volume=. 2022 , publisher=

    2022

  55. [55]

    CNS Neuroscience & Therapeutics , volume=

    Shared and specific dynamics of brain activity and connectivity in amnestic and nonamnestic mild cognitive impairment , author=. CNS Neuroscience & Therapeutics , volume=. 2022 , publisher=

    2022

  56. [56]

    Frontiers in psychology , volume=

    Brain structural and functional changes in cognitive impairment due to Alzheimer’s disease , author=. Frontiers in psychology , volume=. 2022 , publisher=

    2022

  57. [57]

    Brain Imaging and Behavior , volume=

    Different patterns of functional and structural alterations of hippocampal sub-regions in subcortical vascular mild cognitive impairment with and without depression symptoms , author=. Brain Imaging and Behavior , volume=. 2021 , publisher=

    2021

  58. [58]

    Alzheimer's Research & Therapy , volume=

    Static and dynamic functional connectivity variability of the anterior-posterior hippocampus with subjective cognitive decline , author=. Alzheimer's Research & Therapy , volume=. 2022 , publisher=

    2022

  59. [59]

    Network Neuroscience , volume=

    Static and dynamic functional connectome reveals reconfiguration profiles of whole-brain network across cognitive states , author=. Network Neuroscience , volume=. 2023 , publisher=

    2023

  60. [60]

    Alzheimer's Research & Therapy , volume=

    Recurrent and concurrent patterns of regional BOLD dynamics and functional connectivity dynamics in cognitive decline , author=. Alzheimer's Research & Therapy , volume=. 2021 , publisher=

    2021

  61. [61]

    Human Brain Mapping , volume=

    Alterations of voxel-wise spontaneous activity and corresponding brain functional networks in multiple system atrophy patients with mild cognitive impairment , author=. Human Brain Mapping , volume=. 2023 , publisher=

    2023

  62. [62]

    IEEE Transactions on Medical Imaging , volume=

    Deep multi-modal discriminative and interpretability network for alzheimer’s disease diagnosis , author=. IEEE Transactions on Medical Imaging , volume=. 2022 , publisher=

    2022

  63. [63]

    Information Fusion , volume=

    A multiview-slice feature fusion network for early diagnosis of Alzheimer’s disease with structural MRI images , author=. Information Fusion , volume=. 2025 , publisher=

    2025

  64. [64]

    Nature communications , volume=

    Multimodal deep learning for Alzheimer’s disease dementia assessment , author=. Nature communications , volume=. 2022 , publisher=

    2022

  65. [65]

    Alzheimer's & Dementia , volume=

    Memory complaints are associated with impaired working memory and reduced frontal cortical thickness in mid-life surgically menopausal women: Neuropsychology/Early detection of cognitive decline with neuropsychological tests , author=. Alzheimer's & Dementia , volume=. 2020 , publisher=

    2020

  66. [66]

    Alzheimer's & dementia: translational research & clinical interventions , volume=

    Alzheimer's disease drug development pipeline: 2020 , author=. Alzheimer's & dementia: translational research & clinical interventions , volume=. 2020 , publisher=

    2020

  67. [67]

    Nature medicine , volume=

    Plasma P-tau181 in Alzheimer’s disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer’s dementia , author=. Nature medicine , volume=. 2020 , publisher=

    2020

  68. [68]

    IEEE Transactions on Pattern Analysis and Machine Intelligence , volume=

    Learn-explain-reinforce: counterfactual reasoning and its guidance to reinforce an Alzheimer's Disease diagnosis model , author=. IEEE Transactions on Pattern Analysis and Machine Intelligence , volume=. 2022 , publisher=

    2022