BRAIN-INDIA Progression Studio: Deep-Research Strategy Report for the CBR AI Challenge for Healthy Brain Aging
Build one flagship system: BRAIN-INDIA Progression Studio, a multimodal, longitudinal dementia-risk and progression workbench that predicts future MCI/dementia conversion, forecasts cognitive trajectories, and presents a "digital twin lite" view using matched historical trajectories and calibrated uncertainty. The core should use tabulated and extracted features from MRI, OCT/OCTA, cognitive assessments, blood biochemistry, and targeted omics/genomics. Do not make a generic chatbot, a raw-image foundation model, or a genetics-only score the center of the submission. [S1, S2, S3]
The sharp interpretation of the challenge is this: CBR is asking for research acceleration infrastructure plus credible prediction, not just an isolated classifier. The wording explicitly names prediction of MCI/dementia, disease progression, digital twins, and data processing or harmonisation tools; the local inventory confirms that the provided CBR data are already multimodal and substantially tabulated across clinical, blood, cognitive, MRI, eye/vision, autonomic, gait, balance, audiometry, and spirometry streams. [S1, S2]
This recommendation fits a B.Tech CS + bioinformatics team because it leans into Python ML, data engineering, survival modelling, multimodal tabular learning, explainability, and genomics-aware QC. It avoids the least feasible path: training a massive raw MRI/OCT foundation model from scratch under a 12-week competition schedule. [S1, S2, S4, S9]
The single best project name and scope are:
BRAIN-INDIA Progression Studio: a harmonised multimodal risk, progression, and twin-lite workbench for Indian brain-aging research.
The highest-scoring story is: train and benchmark on public longitudinal cohorts; adapt and validate on CBR's rural and urban Indian cohorts; show that the system works when modalities are missing; and report calibration, survival metrics, fairness, uncertainty, and leakage controls instead of only AUROC. [S4, S6, S7, S8, S9, S10, S11, S12, S17]
The provided CBR inventory is more specific than the public cohort webpages and should be treated as the planning source for the challenge data. It describes SANSCOG as a rural Karnataka cohort and TLSA as an urban Bangalore cohort, both prospective and ongoing. It lists SANSCOG at 10,013 participants, average age about 59, with about 4 visits and 2-year follow-up, while TLSA is listed at 1,670 participants, average age about 62, with about 9 visits and yearly follow-up. [S2]
| Data stream | SANSCOG data points | TLSA data points | Design implication |
|---|---|---|---|
| Clinical data | 17,561 | 5,534 | Strong base for risk factors, comorbidity, CDR/HMSE, function, family history, substance use. |
| Blood biochemistry | 15,314 | 5,222 | Feasible low-cost biomarker layer: lipids, glucose, CBC, CRP, liver/kidney function, HbA1c. |
| Cognitive data | 16,134 | 4,478 | Core outcome and progression modelling substrate. |
| MRI | 3,330 | 3,345 | Analysis-ready structured neuroimaging layer, not necessarily raw foundation-model training. |
| Eye and vision | 2,436 | 4,012 | OCT/OCTA/fundus should be an optional augmentation channel, especially strong in TLSA. |
| Autonomic function | 92 | 477 | Exploratory only; too sparse for a core challenge claim. |
| Audiometry | 714 | 2,013 | Useful for multimorbidity and aging phenotype; secondary. |
| Gait | 590 | 1,359 | Secondary mobility/frailty feature block. |
| Balance | 1,850 | 4,020 | Potentially valuable for aging phenotype and fall/frailty modelling. |
| Spirometry | 5,123 | 4,601 | Cardiometabolic/vascular-aging context; useful but not dementia-specific by itself. |
The CBR Data Use Agreement materially affects architecture: participant-level CBR data must stay in secure, approved environments; public-facing AI tools without guaranteed data containment must not process sensitive or participant-level data; outputs must not enable re-identification or reconstruction; and public dissemination using the data requires CBR review and written approval. This means the competition build should be privacy-preserving by design: local pipelines, aggregate demos, synthetic or de-identified demo rows, model cards, and no uploading raw CBR data to public tools. [S3]
| Dataset | Best use in this challenge | Do not use it for | Why it matters |
|---|---|---|---|
| ADNI | Biomarker-rich AD progression modelling; CU/MCI/dementia cohort definitions; MRI/PET/cognition/biofluid/genetics benchmark; model baselines and longitudinal endpoints. | India-specific calibration; retinal/OCT core modelling; population prevalence. | ADNI is a longitudinal multicenter observational study built to validate AD biomarkers for clinical trials, with clinical, imaging, genetic, biofluid, cognitive, and demographic data. [S4, S5] |
| UK Biobank | Population-scale preclinical risk, brain/health/lifestyle/genomics context, blood and imaging-derived features, long-term linked outcomes. | High-resolution symptomatic AD staging by itself; fast access if RAP is unavailable; India-specific transfer without recalibration. | UKB follows 500,000 participants recruited age 40-69, includes broad health, imaging, biomarker, genetic, records, questionnaire, physical, lifestyle, and environmental data; UKB-RAP was listed as closed with phased reopening from September 2026 on the page accessed July 9, 2026. [S6] |
| OASIS | Open MRI-first proof-of-concept, reproducible imaging benchmarks, FreeSurfer/PET processed outputs, rapid demos. | A full competition flagship alone; Indian calibration; blood/omics-heavy modelling. | OASIS provides open neuroimaging datasets; OASIS-3 has 1,378 participants, 2,842 MR sessions, PET scans, clinical/cognitive data, and biomarker context. [S7] |
| ADDI / AD Workbench | Data discovery, secure cloud workspaces, interoperability, harmonisation inspiration, federated/distributed data sharing design. | Treating it as a single cohort to train the model. | ADDI describes AD Workbench as a secure cloud-based interoperability layer with 340+ datasets and tools to access, combine, analyze, and visualize ADRD data. [S8] |
| NACC | External validation for longitudinal clinical prediction; UDS annual visits; multimodal linkage across imaging, genetics, fluid biomarkers, neuropathology. | Estimating U.S. population prevalence/incidence; naive pooling of CSF assays; assuming all imaging is homogeneous. | NACC contains 56,000+ participants, 217,000 clinical assessments, annual UDS follow-up, imaging, genetic/genomic, neuropathology, and biomarker streams; NACC explicitly warns its sample is not population-based and that CSF values need additional harmonisation across assays/centers. [S9] |
| AIBL | Early detection, long follow-up, external validation, cognition/mood/lifestyle plus blood/CSF and neuroimaging. | Indian calibration; retinal-first claims; very large-scale genomics discovery. | AIBL is an ongoing observational cohort launched in 2006, collecting data every 18 months across biomarkers, neuroimaging, cognition, mood, health, and lifestyle, with 3,045 participants as of February 2023. [S10] |
| TLSA | Urban Indian validation/adaptation; frequent annual visits; strong OCT/OCTA, MRI, clinical, cognitive, blood, balance, spirometry, genomics streams. | Training massive raw-image models; discovery-scale genomics alone; assuming it represents rural India. | CBR describes TLSA as a long-term Bangalore aging cohort with clinical, neurocognitive, lifestyle, anthropometric, biochemical, genetic, and multimodal neuroimaging measures; the local inventory lists 1,670 participants and about 9 annual visits. [S2, S11] |
| SANSCOG | Rural Indian generalisation; fairness across rural/urban context; community-based risk/protective factor analysis; large CBR anchor cohort. | Assuming dense MRI/OCT for every participant; short-term event-rich dementia modelling without checking incident counts; raw-image training. | CBR describes SANSCOG as a prospective rural cohort in Karnataka for adults 45+ with projected n=10,000, multimodal assessments, and at least 10 years of follow-up; the inventory lists 10,013 participants. [S2, S12] |
| GenomeIndia | Indian ancestry-aware genomics QC, allele-frequency reference, variant interpretation, ancestry PCs, conservative genetic-risk calibration for Indian cohorts. | Dementia outcome modelling by itself; direct AD progression labels; unvalidated European PRS transfer. | GenomeIndia is designed to catalogue Indian genetic variation from 10,000 representative individuals; the 2025 Nature Genetics paper reports 10,000 healthy unrelated Indians from 83 populations, addressing underrepresentation in global genomics. [S13] |
| YLOPD | Reusable neurodegeneration pipeline ideas: multimodal visit handling, brain/retinal imaging, genomics, biomarkers, longitudinal architecture. | Alzheimer's/dementia model training labels; AD outcome validation. | CBR's YLOPD is a Parkinson's disease longitudinal study with multimodal clinical, cognitive, brain/retinal imaging, GWAS/WGS, blood biomarkers, and exposure data; it is infrastructure-relevant, not a dementia target cohort. [S14] |
The practical dataset strategy is: OASIS for quick open imaging prototypes; ADNI for biomarker-rich modelling; NACC and AIBL for external validation; UK Biobank for population-risk and blood/genetic context if access permits; ADDI for data-discovery and harmonisation logic; TLSA/SANSCOG for the Indian core; GenomeIndia for ancestry-aware genomics; YLOPD only as reusable neurodegeneration infrastructure inspiration.
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BRAIN-INDIA Progression Studio - flagship recommendation.
Build a harmonised multimodal system that predicts 1-year, 3-year, and 5-year risk of MCI/dementia conversion; forecasts cognitive trajectories; retrieves matched historical trajectories; and reports uncertainty, calibration, interpretability, and subgroup performance. This directly maps to the challenge brief and CBR inventory. [S1, S2] -
Cross-cohort harmonisation toolkit for dementia AI.
Build a common data model, data dictionary mapper, visit aligner, unit normalizer, missingness dashboard, train-only harmonisation pipeline, and cohort comparability reports. This is scientifically valuable but weaker as a standalone unless paired with prediction outputs. [S8, S9, S19] -
OCT/OCTA-augmented low-cost triage model.
Use retinal OCT/OCTA and fundus-derived tabular parameters as an augmentation layer beside cognition, blood biochemistry, demographics, and MRI. This is attractive for scalable public-health screening, especially because the CBR inventory includes substantial eye/vision data, but retinal biomarkers should not be the sole flagship claim. [S2] -
GenomeIndia-aware dementia genomics sidecar.
Build a genomics module with APOE, ancestry PCs, curated AD loci, pathway/burden features, and Indian allele-frequency annotation. This is a strong differentiator for a bioinformatics team but should support the multimodal model rather than replace it. [S13] -
Federated/private analytics template for CBR data.
Package the workflow so raw CBR data never leave approved environments: local training scripts, aggregate-only exports, synthetic demo rows, privacy checks, and data lineage. This aligns with the DUA and would impress on governance, but needs a modelling layer to feel complete. [S3, S8] -
Long-term frontier: raw multimodal foundation model or generative patient twin.
This is interesting but not the right core for a B.Tech competition team. It requires large curated raw imaging data, heavy compute, rigorous privacy controls, and external clinical validation. Treat it as a stretch goal after the tabular longitudinal platform is working. [S3, S17]
| Evidence tier | What is strongest | What this means for the build |
|---|---|---|
| Strong, near-term | Longitudinal cognition, diagnosis/state transitions, structured clinical variables, extracted MRI phenotypes, blood biochemistry, and standard risk factors. | Make these the base model and primary validation target. [S2, S4, S5, S9, S10] |
| Moderate, near-term | Multimodal late fusion, survival prediction, dynamic landmarking, calibration, subgroup reporting, and external cohort validation. | Use these to make the project scientifically credible and competition-ready. [S9, S17] |
| Promising but emerging | OCT/OCTA as a retinal augmentation channel; omics beyond APOE/ancestry/pathway summaries; twin-lite trajectory retrieval. | Include as differentiators, but avoid standalone diagnostic claims. [S2, S13] |
| Long-term frontier | Raw 3D MRI/OCT foundation models, causal treatment simulators, fully generative patient twins, and genome-wide discovery in small Indian dementia cohorts. | Present only as stretch goals or future work unless validation evidence is unusually strong. [S3, S17, S18] |
BRAIN-INDIA Progression Studio is feasible because the CBR data are already described as extracted/tabulated multimodal parameters, not a mandate to process every MRI/OCT frame from raw pixels. That lets the team spend the competition window on higher-value work: defining outcomes, harmonising features, preventing leakage, training strong baselines, adding survival/progression models, and building an interpretable demo. [S1, S2]
The best endpoint set is:
- Incident MCI from cognitively unimpaired baseline.
- Incident dementia or AD-type dementia from MCI or high-risk baseline.
- Time-to-conversion when event dates or visit intervals support survival analysis.
- Future cognitive trajectory over 1, 3, and 5 visits using HMSE/MMSE/MoCA/CDR-style or harmonized cognitive composites.
- Meaningful decline thresholds defined before model training, such as drop in a cognitive composite or transition in clinical state.
The key engineering insight: do not force a single "complete-case" model. The winning model should accept missing modalities gracefully. A participant may have cognition and blood data but no MRI; another may have OCT/OCTA and cognitive data; another may have genomics but sparse imaging. Late fusion, modality-specific encoders, missingness indicators, and calibrated model stacking are more robust than an all-or-nothing deep net.
Dementia is longitudinal, heterogeneous, and multimodal. ADNI, NACC, AIBL, OASIS, and the CBR cohorts all reinforce the same lesson: clinically meaningful AI should model change over time, not only classify AD versus control at one visit. ADNI was designed around biomarker validation for AD clinical trials; NACC's UDS is annual and longitudinal; AIBL collects repeated biomarker, imaging, cognitive, mood, and lifestyle data; TLSA/SANSCOG are explicitly built around long-term cognitive aging and dementia risk factors in India. [S4, S5, S9, S10, S11, S12]
The scientifically honest version of a digital twin is not a claim that the system can simulate a person's causal future under arbitrary treatments. The competition-ready version is a digital twin lite: a personalized forecast plus matched real trajectories from similar historical participants. This gives the demo intuitive power while staying defensible.
Judges are likely to value a project that makes the CBR cohorts central instead of treating them as a final leaderboard dataset. TLSA and SANSCOG let the team make an India-relevant contribution: urban-rural transfer, education/language-aware cognitive modelling, cardiometabolic risk, ancestry-aware genomics, and privacy-respecting deployment. GenomeIndia strengthens this story because it makes naive European-default genetics less acceptable for Indian cohorts. [S2, S11, S12, S13]
The clear call: do global benchmark training and method development, then demonstrate Indian adaptation and validation. That is more compelling than a single ADNI classifier with a polished UI.
BRAIN-INDIA should have five layers:
- Secure data layer: local data loaders, data dictionaries, provenance, cohort-specific ingestion, DUA-compliant no-upload workflow.
- Harmonisation layer: common data model, visit alignment, unit normalization, feature mapping, cohort/site/scanner/device metadata, train-only harmonisation.
- Modality encoders: clinical/cognitive, MRI, OCT/OCTA/fundus, blood biochemistry, genomics/omics, aging-phenotype modules.
- Prediction layer: fixed-horizon risk models, survival models, trajectory forecasts, matched-trajectory retrieval, uncertainty estimation.
- Research workbench UI: participant-level risk explanation, cohort dashboard, missingness view, calibration plots, subgroup fairness, and demo twin-lite panel.
Use a relational or parquet-backed schema, not one giant spreadsheet:
participants: person ID, cohort, recruitment context, sex, age/birth proxy, education, language, socioeconomic fields if allowed, ancestry fields if available.visits: visit ID, person ID, visit date or offset, wave number, follow-up interval, diagnosis/state, censoring/event status.modality_features: person ID, visit ID, modality, feature name, value, unit, source table, derivation status, QC flag.outcomes: endpoint, anchor visit, prediction horizon, event indicator, event time/window, future cognitive score.data_dictionary: variable definitions, units, allowed values, missing codes, cohort-specific mappings.provenance: extraction version, script hash, data version/date, cohort, access constraints.
Structured MRI. Start with extracted imaging phenotypes: hippocampal volume, entorhinal/cortical thickness, ventricular volume, WMH burden, regional volumes, diffusion summaries, fMRI summaries if available, PET SUVR/centiloid only in public cohorts where available. Use raw MRI only as a stretch goal. NACC SCAN's analysis results include brain volumes, cortical thickness, surface area, SUVRs, QC summaries, and defaced images, which validates an extracted-feature-first approach. [S9]
OCT/OCTA and fundus. Use retinal thickness, layer measures, vessel density, FAZ, angiography summaries, fundus-derived features, eye-level QA, and device metadata. Treat this as an augmentation channel that may improve low-cost triage, not as a standalone dementia diagnosis engine. The CBR inventory makes this attractive because eye/vision data are substantial, especially in TLSA. [S2]
Cognition. Build both cohort-specific and harmonized features. Keep raw test scores where interpretable, but also create domain composites: global cognition, attention, executive function, language, memory, visuospatial ability, functional status, and CDR/HMSE/MMSE/MoCA-style state. Adjust interpretation for education, language, and rural/urban context.
Blood biochemistry. Use fasting lipid profile, glucose, CBC, CRP, liver/kidney function, HbA1c, and cardiometabolic composites. These should be framed as scalable risk and comorbidity features, not AD-specific biomarkers unless AD-specific assays are present. NACC's CSF warning is a useful principle: assay values from different centers and methods cannot be blindly pooled without harmonisation. [S2, S9]
Omics and genomics. Use a conservative layer: APOE if available; ancestry PCs; curated AD loci; pathway/burden scores; variant QC; GenomeIndia-aware allele-frequency annotation; and omics modules only when sample size supports them. Do not lead with a genome-wide black-box model. GenomeIndia's value is reference and calibration, not direct dementia labels. [S13]
- Define target outcomes before harmonisation. Decide the anchor visits, horizons, conversion definitions, and cognitive decline thresholds before feature engineering to reduce label leakage.
- Create a variable mapping book. Each feature gets a canonical name, unit, cohort-specific source variable, missing-code logic, and allowed transforms.
- Align visits by anchor time. Convert dates into visit offsets from baseline, age at visit, and prediction windows; never let post-anchor data enter pre-anchor features.
- Normalize units and ranges. Explicitly convert lab units, imaging volumes, retinal measures, and cognitive scales; retain original values for audit.
- Model missingness, do not erase it. Use missingness indicators, modality availability flags, and model architectures that degrade gracefully.
- Handle site/scanner/device effects. Use metadata-aware residualization or ComBat-style harmonisation fitted only on training data. Preserve biological covariates such as age and sex. [S19]
- Perform cohort-aware validation. A harmonisation method is only credible if it improves or preserves performance on held-out cohorts, not only internal cross-validation.
- Expose diagnostics. The demo should show missingness heatmaps, distribution shifts, feature overlap, and post-harmonisation checks.
Use three tiers:
- Tier 1 transparent baselines: logistic regression, elastic net, Cox proportional hazards, simple landmark models.
- Tier 2 strong tabular ML: LightGBM/XGBoost/CatBoost, random survival forests, gradient boosting survival models.
- Tier 3 longitudinal models: mixed-effects models, dynamic landmarking, GRU-D or small temporal transformer only after baselines are strong.
Fixed-horizon outputs should include risk at 1, 3, and 5 years or visits, depending on available follow-up. Survival outputs should include time-to-conversion and censoring-aware metrics.
Forecast future cognitive trajectory as a band, not a single number. Start with mixed-effects models and gradient-boosted regressors for future cognitive composite scores. Then add a sequence model if the visit density supports it. The result should show:
- expected future cognitive score;
- 50% and 90% uncertainty intervals;
- top contributing modalities/features;
- matched historical trajectories;
- warnings when the participant is outside the training distribution.
The twin-lite module should be retrieval plus calibrated forecasting:
- Embed each anchor visit into a harmonized latent space using selected multimodal features.
- Retrieve top-k similar historical participants from training cohorts using strict train/test separation.
- Display their observed downstream trajectories as "matched histories."
- Overlay the model's predicted risk and cognitive trajectory band.
- Report similarity confidence and missing-modality caveats.
This is much safer than a generative synthetic patient twin. It is also easier to validate: the model can be judged by calibration, nearest-neighbor outcome consistency, and external trajectory error.
GenomeIndia changes the design in three ways. First, it makes Indian ancestry and population structure a first-class modelling issue. Second, it gives a principled reason not to blindly import European-derived polygenic risk scores. Third, it lets the team build a bioinformatics differentiator without overclaiming causal genetics. [S13]
The near-term genomics module should include:
- ancestry-aware QC: call rate, relatedness, sex check, heterozygosity, ancestry PCs;
- APOE encoding where available;
- curated AD risk loci and pathway-level summaries;
- rare-variant burden only for genes/pathways with enough sample support;
- Indian allele-frequency annotation using GenomeIndia resources when allowed;
- interaction checks with age, sex, education, rural/urban status, and cardiometabolic risk;
- calibration comparison: model without genetics, model with APOE only, model with conservative genomics, and model with omics sidecars.
Avoid claiming that GenomeIndia is an Alzheimer's cohort. It is a population genomics reference. Its role is to make Indian cohort genomics more accurate, interpretable, and ethically defensible. [S13]
Use a biomedical AI evaluation panel, not a leaderboard-only panel:
- Fixed-horizon prediction: AUROC, AUPRC, sensitivity/specificity at clinically meaningful thresholds, balanced accuracy, decision-curve analysis if feasible.
- Survival modelling: C-index, time-dependent AUC, integrated Brier score, calibration by time horizon.
- Trajectory forecasting: MAE/RMSE, correlation, prediction interval coverage, calibration of uncertainty intervals.
- Calibration: calibration slope, calibration-in-the-large, ECE/Brier score, reliability plots by cohort and horizon.
- External validation: hold out at least one public cohort and, when allowed, report TLSA/SANSCOG separately rather than only pooled CBR results.
- Subgroup fairness: sex, age band, education, cohort, rural/urban, baseline cognitive state, modality availability, and ancestry/genetic strata where allowed.
- Interpretability: SHAP or permutation importance by feature and modality; patient-level explanation; matched-trajectory evidence.
- Uncertainty: confidence intervals, conformal/quantile bands, out-of-distribution warnings.
- Leakage prevention: participant-level splits, anchor-time feature windows, no future visits in features, train-only imputation/harmonisation, no scaling on full data, and no near-duplicate leakage across visits.
TRIPOD+AI-style reporting is useful as a checklist for transparent prediction-model development and validation, especially around data sources, participants, outcomes, predictors, sample size, missing data, model specification, performance, calibration, and code reproducibility. [S17]
Judges will likely reward:
- a clear India-first reason the project matters;
- serious use of TLSA/SANSCOG rather than public data only;
- multimodal longitudinal modelling that matches the challenge wording;
- strong baselines and external validation;
- calibration and uncertainty, not just headline accuracy;
- privacy and DUA compliance;
- a clean demo that explains "why this risk, why this trajectory, and what similar participants did";
- a reproducible pipeline with documented data dictionaries, model cards, and benchmark reports.
Avoid these as the core submission:
- a generic dementia chatbot;
- AD-vs-control classifier on one public dataset;
- raw MRI/OCT foundation model from scratch;
- full causal digital twin or treatment simulator without validation;
- European PRS transplanted to Indian cohorts without recalibration;
- complete-case-only modelling that discards most participants;
- leakage through future visits, full-data normalization, or subject-overlapping splits;
- claims of clinical diagnosis or screening deployment when the system is a research tool;
- uploading participant-level CBR data to public AI services or any non-approved environment. [S3]
Assume a 5-person B.Tech team:
- Lead/data architect: common data model, ingestion, data dictionary, reproducibility.
- ML lead: baselines, boosting, survival models, calibration.
- Longitudinal lead: trajectory forecasting, dynamic landmarking, twin-lite retrieval.
- Bioinformatics lead: genomics QC, APOE/loci/pathways, GenomeIndia-aware annotation.
- Product/evaluation lead: dashboard, model cards, subgroup/fairness reports, final demo.
| Week | Milestone | Deliverables |
|---|---|---|
| 1 | Scope lock and ethics setup | Final endpoints, DUA-safe workflow, repository skeleton, source/data inventory, risk register. |
| 2 | Data model and dictionaries | Participant/visit/modality/outcome schema, variable mapping template, missingness codebook. |
| 3 | Public cohort prototype | OASIS/ADNI starter extraction, baseline cognitive/MRI model, subject-level split tests. |
| 4 | CBR schema adapter | TLSA/SANSCOG ingestion adapters, inventory-aligned feature groups, secure local run instructions. |
| 5 | Harmonisation v1 | Unit normalization, visit alignment, train-only imputation/scaling, missingness dashboard. |
| 6 | Baseline risk models | Logistic/elastic-net/Cox/LightGBM models, AUROC/AUPRC/C-index, first calibration plots. |
| 7 | Progression models | Mixed-effects and gradient-boosted trajectory forecasts, interval coverage reports. |
| 8 | Multimodal fusion | Modality-specific encoders, missing-aware stacking, ablation by modality and cohort. |
| 9 | Twin-lite retrieval | Similar-patient embedding, top-k trajectory panel, retrieval validation, OOD warnings. |
| 10 | Genomics sidecar | APOE/PC/pathway module, GenomeIndia-aware notes, genetics ablation and calibration check. |
| 11 | External validation and fairness | Held-out cohort tests, TLSA vs SANSCOG reporting, sex/age/education/rural-urban subgroup panels. |
| 12 | Demo and final package | Streamlit/FastAPI demo, model card, benchmark checklist, slide-ready figures, reproducible command script. |
Exact project name: BRAIN-INDIA Progression Studio.
One-line scope: A privacy-aware, harmonised multimodal workbench that predicts dementia/MCI risk, forecasts cognitive progression, and displays matched longitudinal "twin-lite" trajectories for Indian brain-aging research.
System architecture: Local secure data ingestion; common data model; train-only harmonisation; modality-specific feature encoders; fixed-horizon risk models; survival models; trajectory forecasting; similar-participant retrieval; dashboard with calibration, explanations, uncertainty, and subgroup reports.
Novelty angle: The novelty is not "AI predicts dementia." The novelty is Indian-cohort-aware longitudinal multimodal dementia modelling with harmonisation, uncertainty, and twin-lite retrieval, grounded in TLSA/SANSCOG and strengthened by public cohorts and GenomeIndia-aware genomics.
Demo plan: Show three synthetic/de-identified participant profiles:
- Urban TLSA-like participant with cognition, blood, MRI, OCT/OCTA, and genomics.
- Rural SANSCOG-like participant with cognition, clinical, blood, spirometry, and partial imaging.
- Public-cohort ADNI/NACC-like participant for external benchmark comparison.
For each, the demo should show current risk, 1/3/5-year conversion risk, predicted cognitive trajectory, uncertainty band, top feature drivers, missing-modality caveats, and matched historical trajectories.
Minimum viable version: A working local pipeline that:
- ingests at least one public cohort plus a CBR-schema mock or approved sample;
- maps features into the common data model;
- trains baseline fixed-horizon and survival models;
- reports calibration and subgroup metrics;
- produces a twin-lite matched-trajectory panel;
- includes DUA-safe demo data and documentation.
Stretch goals: OCT/OCTA ablation, genomics sidecar, federated/ADDI-compatible packaging, advanced temporal model, raw-image feature extraction, and richer dashboard deployment.
- Data: Python, pandas, polars, pyarrow, pydantic, pandera, DuckDB.
- ML: scikit-learn, LightGBM/XGBoost/CatBoost, lifelines, scikit-survival, statsmodels.
- Longitudinal: mixed-effects models, landmarking utilities, PyTorch for GRU-D/temporal transformer stretch.
- Harmonisation: neuroCombat/neuroHarmonize-style ComBat workflows, custom residualization, cohort shift diagnostics. [S19]
- Genomics: PLINK2, bcftools, Hail for larger-scale public genomics, pandas/pyarrow for feature sidecars.
- Explainability: SHAP, permutation importance, partial dependence, feature-group attribution.
- Uncertainty: bootstrapping, conformal prediction, quantile regression, calibration curves.
- Dashboard: Streamlit or Dash for speed; FastAPI if an API demo is needed.
- Reproducibility: DVC or git-annex for metadata, Docker/conda/uv, Makefile or nox, model cards, data cards.
brain-india-progression-studio/
README.md
configs/
cohorts/
features/
endpoints/
data_dictionary/
common_data_model.yaml
variable_mapping_template.csv
src/
ingest/
harmonize/
features/
models/
evaluation/
twin_lite/
privacy/
dashboard/
notebooks/
01_data_audit.ipynb
02_baseline_models.ipynb
03_external_validation.ipynb
reports/
data_inventory_audit.md
model_card.md
benchmark_report.md
tests/
test_no_future_leakage.py
test_subject_split.py
test_harmonization_train_only.py
test_schema_validation.py
app/
streamlit_app.py
- Participant-level splits, no visit leakage.
- Prediction anchor and horizon defined before feature extraction.
- Imputation/scaling/harmonisation fitted only on training data.
- Missingness and modality availability reported.
- AUROC/AUPRC for fixed horizons.
- C-index, time-dependent AUC, integrated Brier score for survival.
- Calibration slope/intercept and reliability plots.
- External cohort validation.
- TLSA and SANSCOG reported separately when available.
- Sex, age, education, cohort, rural/urban subgroup metrics.
- Feature and modality ablations.
- SHAP/permutation explanations.
- Uncertainty intervals and OOD warnings.
- DUA-compliant data handling.
- Model card and data card.
- Reproducible run command.
Local challenge and CBR-provided files
- S1. Challenge brief and goal objective, local attachment:
C:\Users\adity\.codex\attachments\2327bc2b-a372-419d-8ff6-a57c65ef8bba\goal-objective.mdandpasted-text-1.txt. - S2. CBR AI Challenge Dataset Inventory, local PDF:
C:\Users\adity\OneDrive\Desktop\IISc\CBR\Hackathon\Dataset_Inventory_AI_Challenge.pdf. - S3. CBR Data Use Agreement, local PDF:
C:\Users\adity\OneDrive\Desktop\IISc\CBR\Hackathon\Data_Use_Agreement.pdf.
Official cohort and platform sources
- S4. ADNI home page and data overview: ADNI and ADNI Data.
- S5. ADNI cohort definitions: Study Cohort Information.
- S6. UK Biobank: home, about data, types of data, Research Analysis Platform.
- S7. OASIS Brains: Open Access Series of Imaging Studies.
- S8. Alzheimer's Disease Data Initiative: ADDI home.
- S9. NACC: home, longitudinal UDS data, imaging data, fluid biomarker data, genetic/genomic data.
- S10. AIBL: Australian Imaging, Biomarker and Lifestyle Study of Ageing.
- S11. CBR-TLSA: CBR-TATA Longitudinal Study of Aging.
- S12. CBR-SANSCOG: Srinivaspura Aging, Neuro Senescence and Cognition Study.
- S13. GenomeIndia: CBR GenomeIndia page and Bhattacharyya et al., 2025, Nature Genetics: Mapping genetic diversity with the GenomeIndia project.
- S14. CBR-YLOPD: Young and Late-onset Parkinson's Disease Study.
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