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Data-driven techniques for translational neuroscience and personalized neuro-health

Vishal Subedi, Shashipraba N. K. Rajakaruna, Pratyusha Sarkar, Subhankar Chattoraj, Anjali Khasa, Siddhartha Nandy, Hamza Farooq, Animikh Biswas, Sanjay Chaudhuri, Asim K. Dey, Karuna Joshi, Christophe Lenglet, Ansu Chatterjee

arXiv:2608.13749v1q-bio.NCcs.AIcs.LGstat.APstat.ML

TL;DR

Early diagnosis of neurodegenerative disease requires more sensitive and individualized analysis of brain changes. This review synthesizes complementary data-driven approaches and concludes that they collectively support personalized neuro-health models, while substantial neuroimaging-AI limitations remain.

  • Problem

    Early diagnosis of Alzheimer’s and Parkinson’s disease remains difficult when therapeutic interventions may be most effective, motivating more sensitive and individually meaningful quantitative methods.

  • Method

    The review organizes classical statistics, geometric and topological representation, artificial intelligence, and mechanistic dynamical modeling into four complementary methodological pillars.

  • Results

    Across the four pillars, the review identifies complementary tools addressing different facets of the shared translational goal of personalized neuro-health.

  • Takeaways & Limitations

    Personalized neuro-health is best pursued by combining methodologically diverse approaches rather than treating them as competing alternatives.

  • Takeaways & Limitations

    Neuroimaging AI remains constrained by small samples, high dimensionality, uncertain accuracy estimates, and documented data leakage.

Abstract

from arXiv · show

Neurodegenexrative diseases such as Alzheimer's disease and Parkinson's disease are diagnosed most reliably only after substantial, often irreversible, neuronal loss has already occurred, creating an urgent need for quantitative tools that can detect subtle, early, and individual-specific brain changes from neuroimaging data. This review surveys a broad and rapidly evolving toolkit of data-driven techniques for translational neuroscience and personalized neuro-health, organized around four complementary methodological pillars. Throughout, we emphasize how these methodologically diverse approaches converge on a common translational goal: personalized, mechanistically grounded, and clinically actionable models of individual brain health, and we close by discussing the principal open statistical, computational, and clinical challenges that remain.

1 INTRODUCTION

Neurodegenerative diseases pose an urgent early-diagnosis challenge because Alzheimer’s and Parkinson’s disease are often identified only after substantial neuronal loss. This review surveys four complementary data-driven pillars that converge on personalized neuro-health: neuroimaging analysis, geometric and topological methods, artificial intelligence, and mechanistic dynamical modeling.

  • Clinical motivation: Alzheimer’s disease and Parkinson’s disease are prevalent neurodegenerative disorders whose early diagnosis remains difficult, when therapeutic interventions may be most effective.The diseases impose substantial social and economic consequences worldwide.
  • Methodological pillars: The review’s first pillar covers fMRI analysis and dimension-reduction techniques, beginning with mass-univariate regression and statistical parametric mapping.This framework is linked to the characterization and formalization of the blood-oxygen-level-dependent (BOLD) contrast mechanism.
  • Methodological pillars: The second pillar applies graph theory, network science, differential geometry, and topology to characterize brain-network geometry, robustness, fragility, and multiscale organization.It includes discrete Ricci curvature and persistent homology, with applications spanning healthy aging, neurological and psychiatric conditions, tumor morphology, and Alzheimer’s disease classification.
  • Methodological pillars: The third pillar surveys artificial intelligence, including deep, recurrent, hybrid, multimodal, and generative architectures, for neurodegenerative disease diagnosis, staging, and progression modeling.The reviewed applications focus on Alzheimer’s and Parkinson’s disease.
  • Methodological pillars: The fourth pillar models neuroimaging and neurodegenerative disease progression as evolving dynamical systems, spanning biophysical, neural-mass, whole-brain, statistical, and machine-learning models.This perspective contrasts with largely stationary, localized statistical descriptions.
  • Translational goal: Across the four pillars, classical inference, geometric and topological representation, artificial intelligence, and mechanistic dynamical modeling are complementary tools serving personalized neuro-health.Each addresses a different facet of the shared translational goal.

2 FMRI DATA ANALYSIS AND DIMENSION REDUCTION TECHNIQUES

This section presents fMRI as a quantitative, four-dimensional measure of hemodynamically mediated neural activity and reviews classical statistical modeling alongside Bayesian, empirical-likelihood, and dimension-reduction approaches. It emphasizes that conventional unsupervised decompositions can overlook subject-level covariates, motivating methods that link lower-dimensional functional structure to demographic or clinical traits.

  • fMRI data and preprocessing: fMRI measures neural activity indirectly through the hemodynamically mediated BOLD contrast and is typically collected during tasks or resting states.The data are indexed by three spatial dimensions plus time and may include task, demographic, structural, EEG, and MEG information.
  • Classical statistical analysis: The classical fMRI analysis framework fits a mass-univariate linear regression model at each voxel to produce whole-brain statistical parametric maps of experimental effects.Voxel responses are modeled as time series with task-related covariates, and noise may be temporally dependent or modeled autoregressively.
  • Statistical inference: Classical statistical inference depends strongly on model assumptions and requires multiple-comparison corrections, whose reliability has been critically examined.The framework may also incorporate spatial, temporal, within-subject, and multi-subject dependencies through Bayesian models.
  • Dimension reduction: PCA and ICA can extract latent functional networks, but ignoring subject-level covariates may emphasize population variance or scanner noise over circuits relevant to personalized medicine.The limitation motivates covariance-based approaches that explicitly model heterogeneity associated with demographic or clinical traits.
  • Covariance-based dimension reduction: CAP integrates PCA with generalized linear modeling for covariance-matrix outcomes, while BCAP simultaneously extracts a covariate-linked lower-dimensional subspace on the Riemannian manifold of covariance matrices.BCAP addresses the non-Euclidean geometry of covariance matrices and explicitly links covariance heterogeneity to covariates.
  • Illustrative experiment: In an n = 16 MNI152-masked experiment, SSGP showed no significant demographic association, whereas GICA and SCFP identified functional connectivity findings.Age, Sex, and their interaction were used as demographic features, and posterior credible intervals for SSGP included zero.

3 APPROACHES USING GRAPHS, NETWORKS, TOPOLOGY, GEOMETRY · 3.1 Network curvature in structural and functional brain connectomics

Network-curvature methods extend conventional brain-network analysis by modeling intrinsic geometry, enabling characterization of structural robustness, functional abnormalities, aging-related changes, and macroscale organization. The section also presents theoretical extensions and computational frameworks supporting broader translational use of geometric network analysis.

  • 3 APPROACHES USING GRAPHS, NETWORKS, TOPOLOGY, GEOMETRY · 3.1 Network curvature in structural and functional brain connectomics: Discrete Ricci curvature complements traditional graph measures by quantifying intrinsic network geometry through how neighboring regions interact via probability transport.This geometric perspective targets information flow, robustness, and resilience, which conventional measures capture only incompletely.
  • 3.1.1 Architectural robustness of structural brain networks: In structural connectomics, positive Ollivier-Ricci curvature indicates robust organization, whereas negative curvature marks structurally fragile regions vulnerable to disruption.Curvature treats dMRI-derived connectivity as a geometric landscape and provides an interpretation of architectural stability and physical vulnerability.
  • 3.1.1 Architectural robustness of structural brain networks: Curvature detected local and global treatment-associated changes in structural robustness after autologous umbilical cord blood infusion in children with ASD, including changes missed by traditional graph analyses.The findings support greater sensitivity of geometric descriptors for monitoring subtle structural-network changes.
  • 3.1.2 Applications to functional brain networks: Forman-Ricci and Ollivier-Ricci curvature provide complementary geometric descriptions of functional-network organization and identify ADHD-associated network anomalies.Applications to resting-state and dynamic fMRI networks extend curvature analysis from structural to functional interactions.
  • 3.1.2 Applications to functional brain networks: Curvature analysis revealed clinically relevant functional-connectivity alterations missed by conventional edge-weight analyses and identified widespread, region-specific alterations in ASD using ABIDE data.These results indicate that geometric metrics capture higher-order organizational features of functional connectivity.
  • 3.1.3 Characterizing Healthy Aging and Community Detection: In healthy aging, regional Ollivier-Ricci and Forman-Ricci curvature changes occur mainly in movement, somatosensory, and affective-processing regions and correlate directly with behavioral measures.This links functional-network geometry to age-related brain organization and behavior.
  • 3.1.3 Characterizing Healthy Aging and Community Detection: Ricci-flow community detection showed that community-detection accuracy and robustness depend strongly on underlying network topology when compared with modularity maximization and Bayesian stochastic block models across large-scale fMRI datasets.The result establishes Ricci flow as a geometric framework for macroscale network organization.
  • 3.1.4 Theoretical Extensions: Theoretical extensions apply curvature-based geometric phase-transition frameworks across structural organization, functional dynamics, and disease-related alterations in biomedical and brain networks.The framework extends beyond brain networks to biomedical knowledge graphs and disease comorbidity networks.

3.2 Topological data analysis (TDA) and related developments in neuroscience

Topological data analysis (TDA) provides multiscale representations of neural activity, connectivity, and brain structure by converting neuroscience data into combinatorial structures and tracking persistent features across filtrations. In neuroscience, TDA supports morphology-sensitive structural MRI analysis, topology-aware statistical modeling and segmentation, and multiscale analysis of time-varying activity and brain networks.

  • 3.2 Topological data analysis (TDA) and related developments in neuroscience: TDA transforms point clouds, functions, and images into simplicial or cubical complexes whose vertices, edges, triangles, and higher-dimensional elements encode data relationships.Cubical complexes are particularly suited to image data represented on regular grids, including MRI voxels.
  • 3.2 Topological data analysis (TDA) and related developments in neuroscience: Persistent homology records when topological features are born and die across filtrations, yielding persistence diagrams that compactly represent multiscale structure.Persistence diagrams encode features by their birth and death scales, including connected components and one-dimensional features.
  • 3.2 Topological data analysis (TDA) and related developments in neuroscience: Intensity-based structural MRI filtrations are simple and computationally convenient but can be distorted by noise, scanner-dependent contrast variation, and intensity non-uniformity.These artifacts may alter the filtration order and affect the resulting topological summaries.
  • 3.2 Topological data analysis (TDA) and related developments in neuroscience: Topology-aware structural MRI methods use descriptors such as SECT, persistence surfaces, and persistent-homology priors for survival prediction, functional inference, and unsupervised anatomical segmentation.SECT represents tumor shape as a smoothed function-valued descriptor, persistence surfaces provide stable functional embeddings, and cubical persistent homology can guide train-free MRI segmentation.
  • 3.2 Topological data analysis (TDA) and related developments in neuroscience: For fMRI and brain networks, persistent homology characterizes evolving activity topology and supports global, multiscale inference through Betti curves, ratio statistics, and expected persistent barcodes.These approaches analyze time-resolved activity, white matter structural covariance networks, and population-level network topology while avoiding threshold-based multiple-comparison problems.

3.3 Knowledge Graph representations for Clinical Interoperability

Knowledge Graphs integrate fragmented biomedical evidence through semantically labelled entities and relations, supporting interoperability and clinically actionable translational neuroscience. Hypergraphs extend this representation to higher-order interactions that capture multimodal disease signatures and enable patient stratification, hypothesis generation, and biomarker discovery.

  • Clinical interoperability: Semantic Web techniques can model and reason over data, image-analytical models, and sharing policies so software agents communicate using shared meanings.This interoperability requirement covers both data exchange and the description of valid data use.
  • Knowledge Graph representations: Knowledge Graphs represent biomedical entities and relations across publications, ontologies, and specialised repositories, addressing evidence fragmentation in translational neuroscience.They model proteins, molecular pathways, anatomical structures, drugs, phenotypes, and diseases as semantically linked nodes.
  • Knowledge Graph representations: Encoding genetic risk, proteinopathy, atrophy, network disruption, and cognitive decline in graphs supports patient stratification, mechanistic hypothesis generation, and biomarker–disease association discovery.Alzheimer’s disease illustrates the need to connect multi-scale molecular, anatomical, network, and clinical entities.
  • Hypergraph representations: Pairwise graph edges can obscure joint interactions among multiple entities, motivating hypergraphs whose hyperedges directly represent arbitrary-order biological and clinical relationships.This preserves higher-order patterns that may be biologically meaningful for diagnosis and mechanism discovery.
  • Hypergraph representations: Hypergraph spectral learning and hyperedge convolution propagate information across higher-order relations while fusing multiple data modalities in a single model.In neuroimaging cohorts, aligned modality-specific hypergraph views have been used to diagnose Alzheimer’s disease from incomplete multimodality data.

4 ARTIFICIAL INTELLIGENCE TECHNIQUES FOR NEUROSCIENCE STUDIES · 4.1 AI for neuroimage modeling · 4.2 Deep Learning for Diagnosis and Progression Modeling of Neurodegenerative Disease

Artificial intelligence enables individualized neuroimage analysis by learning features from multimodal clinical data and generating synthetic data. Deep-learning architectures support diagnosis, progression modeling, longitudinal change detection, and pathology validation in neurodegenerative disease.

  • 4.1 AI for neuroimage modeling: AI is central to neuroimage analysis because conventional structural imaging and clinical assessment often detect neurodegenerative disease only after substantial neuronal loss.The review frames early, individualized detection as a central need in neurodegenerative disease.
  • 4.1 AI for neuroimage modeling: Deep-learning models learn hierarchical features directly from high-dimensional multimodal data, including imaging, speech, and gait, without hand-engineered feature extraction.Generative AI complements these models through synthetic data generation, augmentation, and disease modeling.
  • 4.2 Deep Learning for Diagnosis and Progression Modeling of Neurodegenerative Disease: Volumetric convolutional architectures remain the workhorse for neuroimaging-based Alzheimer’s and Parkinson’s disease diagnosis.Three-dimensional CNNs distinguish Parkinson’s and Alzheimer’s disease from T1-weighted MRI volumes, while CNN ensembles support multi-stage Alzheimer’s detection across disease severity.
  • 4.2 Deep Learning for Diagnosis and Progression Modeling of Neurodegenerative Disease: Transfer learning from pretrained VGG, ResNet, and DenseNet architectures is useful for Parkinson’s disease because disease-specific cohorts are typically small.The passage specifically identifies transfer learning as useful in small Parkinson’s disease cohorts.
  • 4.2 Deep Learning for Diagnosis and Progression Modeling of Neurodegenerative Disease: Recurrent and hybrid architectures model the temporal and multimodal character of disease progression, while multimodal fusion generally outperforms single-modality, single-architecture models.Hybrid CNN–LSTM systems report higher Alzheimer’s detection accuracies, and MRI–PET CNN–LSTM frameworks capture structural and temporal disease signatures jointly.
  • 4.2 Deep Learning for Diagnosis and Progression Modeling of Neurodegenerative Disease: Longitudinal change detection compares scans from the same individual using classical registration, subtraction, change-vector, dictionary-learning, and clustering-based methods.These approaches preceded deep learning and were extended to applications including diffusion MRI and multiple-sclerosis lesion tracking.
  • 4.2 Deep Learning for Diagnosis and Progression Modeling of Neurodegenerative Disease: Deep learning also maps tau protein at large histological scale, linking macroscopic imaging-based progression tracking with microscopic validation of underlying pathology.This application bridges imaging-based disease progression and histological pathology.

4.3 Foundation Models and Generative AI for Neuroimaging

Foundation models pretrained through self-supervision on aggregated neuroimaging data are emerging as adaptable backbones for downstream tasks, while generative AI is being applied to synthetic neuroimage generation, augmentation, and disease-progression modeling in Alzheimer’s and Parkinson’s disease.

  • Foundation Models and Generative AI for Neuroimaging: BrainLM was pretrained on 6,700 hours of aggregated fMRI and supports fine-tuned clinical-variable prediction and zero-shot identification of intrinsic functional networks.The zero-shot identification uses learned attention patterns.
  • Foundation Models and Generative AI for Neuroimaging: GANs, VAEs, and diffusion models are being applied to synthetic neuroimage generation, augmentation, and disease-progression modeling in Alzheimer’s and Parkinson’s disease.Reported applications include addressing class imbalance, rare-subtype limitations, and modeling Alzheimer’s disease progression.

4.4 AI, Digital Twins, and Digital Clones of the Brain

AI-driven brain digital twins are personalized generative computational models calibrated to individual imaging data, designed to simulate, predict, and eventually help steer brain health. They combine individualized anatomy and connectivity with whole-brain modeling for clinical inference, while facing substantial personalization, validation, and regulatory challenges.

  • AI, Digital Twins, and Digital Clones of the Brain: Brain digital twins are personalized generative computational models calibrated to an individual’s imaging data to simulate, predict, and eventually help steer that individual’s brain.The virtual-brain-twin pipeline assembles subject-specific structural and functional imaging data into individualized models.
  • AI, Digital Twins, and Digital Clones of the Brain: These models combine personalized brain geometry and connectivity with whole-brain simulation to support clinical inference in healthy ageing and Alzheimer’s and Parkinson’s disease.The approach builds on The Virtual Brain platform for connectome-constrained whole-brain simulation.
  • AI, Digital Twins, and Digital Clones of the Brain: AI supports digital-twin construction through deep-learning segmentation, registration, and parcellation, while foundation models enable fine-tuning to individual scans with comparatively little subject-specific data.These methods address the small-sample calibration problem that can otherwise limit personalization.
  • AI, Digital Twins, and Digital Clones of the Brain: Sparse individual imaging data create identifiability and overfitting risks, while deep-learning surrogates require validation against the mechanistic simulations they replace and clinical-regulatory validation before individualization.Clinical outcomes alone are insufficient for validating surrogates that mimic mechanistic models.

4.5 Change detection in medical images

Change detection in longitudinal medical images automates the identification of clinically meaningful differences, reducing manual comparison and improving inter-observer agreement. Methods span registration and statistical difference modeling, automatically learned deep features, temporal sequence models, and continuous-time disease-progression prediction.

  • 4.5 Change detection in medical images: Change detection identifies differences between successive medical images to reduce manual comparison, increase inter-observer agreement, and focus attention on clinically meaningful changes.Medical imaging supports diagnosis, treatment planning, and continuous disease monitoring.
  • 4.5 Change detection in medical images: Image registration aligns successive images, while digital subtraction and intensity-distribution modeling reveal image changes but remain vulnerable to noise and inaccuracies.Registration eliminates positional differences before comparison.
  • 4.5 Change detection in medical images: Deep learning pipelines automatically learn image features, including dictionary-PCA fusion and siamese neural networks for detecting changes and learning image similarities.The dictionary-PCA approach targets brain MRI changes while minimizing positional variation.
  • 4.5 Change detection in medical images: Temporal sequence models exploit longitudinal imaging, using longitudinal pooling to predict progressive changes and unsupervised synthetic lesion alterations to generate pseudo-labels.These methods address progressive changes in AD, alcohol use disorder, and adolescent neurodevelopment, and limited longitudinal labels for lesion detection.
  • 4.5 Change detection in medical images: Continuous-time models convert baseline images into initialization parameters for an ODE whose solution predicts continuously changing states and segmentation for geographic atrophy and AD.This scheme is designed for long-term disease progression prediction.

4.6 Limitations · 4.7 Open Problems and Future Directions

Neuroimaging AI is constrained by small cohorts, high-dimensional features, uncertain accuracy estimates, and limited gains over classical models. Future work must characterize foundation-model scaling and develop demonstrably faithful interpretability methods.

  • 4.6 Limitations: Neuroimaging AI studies often combine small sample sizes with high-dimensional features, producing substantial uncertainty in cross-validated accuracy estimates.Reproducible brain–behavior associations require cohorts far larger than the median published study.
  • 4.6 Limitations: Reproducible brain–behavior associations require far larger cohorts than the median published neuroimaging study.
  • 4.6 Limitations: Deep networks provide only modest gains over well-tuned classical models at real neuroimaging sample sizes.
  • 4.7 Open Problems and Future Directions: Open problems include characterizing neuroimaging-specific scaling laws for foundation models such as BrainLM, Brain-JEPA, and LaBraM.The required pretraining scale remains unclear.
  • 4.7 Open Problems and Future Directions: It remains unclear what pretraining scale would close the performance gap between deep learning and classical methods.
  • 4.7 Open Problems and Future Directions: Interpretability methods must progress from being merely available to being demonstrably faithful.Architecture-level constraints, including BrainGNN’s region-selection pooling, are identified as one direction.

5 DYNAMICAL SYSTEMS AND MECHANISTIC MODELING OF NEUROIMAGE DATA

Dynamical-systems models represent neural activity and neurodegenerative pathology as time-evolving processes shaped by anatomical connectivity, extending static analyses toward mechanistic and causal explanations. Major challenges include multiscale disease dynamics, sparse longitudinal data, parameter identifiability, and development of personalized digital brain twins.

  • Dynamical systems motivation: Neural activity, functional connectivity, protein aggregates, and tissue atrophy evolve continuously over time under the influence of anatomical connectivity.Static localized analyses identify regions or networks associated with cognition but not mediation’s temporal evolution or controlling mechanism.
  • Modeling frameworks: Neuroimaging dynamical systems are typically nonlinear, stochastic state-space models spanning single-neuron, neural-mass, whole-brain network, and dynamic-causal formulations.Whole-brain models couple regional neural-mass models through structural connectomes measured with diffusion MRI tractography; DCM provides a low-dimensional, hypothesis-driven effective-connectivity framework.
  • Disease progression: Disease-progression models operate over months to decades, whereas neural-activity models typically operate over milliseconds to seconds, while pathology unfolds across genetic, cellular, tissue, and organ scales.Neurodegenerative diseases share accumulation of misfolded protein aggregates that emerge in characteristic regions and progress along anatomical pathways.
  • Open challenges: Key scientific challenges are identifying where and when pathology begins, determining whether accompanying neuronal changes are compensatory or degenerative, and distinguishing these possibilities from limited longitudinal data.These questions require causal models that account for pathology emergence and neuronal activity across time.
  • Open challenges: High-dimensional joint estimation and uncertainty quantification remain difficult with sparse longitudinal imaging, especially when distinct parameters yield similar trajectories and disease-onset time is unknown.The first scan may occur years after the true onset, creating a major identifiability problem.
  • Future directions: Digital brain twins are envisioned as personalized, generative, adaptive whole-brain models calibrated to individual structural and functional imaging for scientific and clinical inference.Developing such individually calibrated models remains a major challenge.

6 CONCLUDING COMMENTS

The review surveys a diverse toolkit for translational neuroscience and personalized neuro-health that converges on earlier detection, individualized brain characterization, and mechanistically interpretable, clinically actionable disease models. It also identifies statistical and methodological challenges that must be addressed for reliable clinical translation.

  • The reviewed methods converge on earlier and more sensitive neurodegenerative-disease detection, individually meaningful brain characterization, and mechanistically interpretable, clinically actionable disease-progression models.
  • Across the review, brain-activity analysis shifts from population-level, stationary descriptions toward personalized and dynamic characterizations linked to demographic and clinical covariates.
  • Clinical translation remains limited by model misspecification, multiple comparisons, and concerns about thresholding and inference reliability in classical and Bayesian regression.
  • Classical and Bayesian regression, empirical likelihood, dimension reduction, network curvature, topological analysis, knowledge graphs, artificial intelligence, and dynamical-system modeling form an interconnected translational agenda.
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