Multi-feature computational framework for combined signatures of dementia in underrepresented settings

Abstract

Objective: The differential diagnosis of behavioral variant frontotemporal dementia (bvFTD) and Alzheimer’s disease (AD) remains challenging in underrepresented, underdiagnosed groups, including Latinos, as advanced biomarkers are rarely available. Recent guidelines for the study of dementia highlight the critical role of biomarkers. Thus, novel cost-effective complementary approaches are required in clinical settings. Approach: We developed a novel framework based on a gradient boosting machine learning classifier, tuned by Bayesian optimization, on a multi-feature multimodal approach (combining demographic, neuropsychological, MRI, and EEG/fMRI connectivity data) to characterize neurodegeneration using site harmonization and sequential feature selection. We assessed 54 bvFTD and 76 AD patients and 152 healthy controls (HCs) from a Latin American consortium (ReDLat). Main results: The multimodal model yielded high AUC classification values (bvFTD patients vs. HCs: 0.93 (±0.01); AD patients vs. HCs: 0.95 (±0.01); bvFTD vs. AD patients: 0.92 (±0.01)). The feature selection approach successfully filtered non-informative multimodal markers (from thousands to dozens). Results proved robust against multimodal heterogeneity, sociodemographic variability, and missing data. Significance: The model accurately identified dementia subtypes using measures readily available in underrepresented settings, with a similar performance than advanced biomarkers. This approach, if confirmed and replicated, may potentially complement clinical assessments in developing countries.

Frontotemporal dementia presentation in patients with heterozygous p.H157Y variant ofTREM2

“…In assessing TREM2 gene expression profiles of brain regions in p.H157Y mutation carriers, we observed a high spatial correspondence between their expression throughout the brain and the atrophy pattern in each case, including frontal (orbitofrontal and cingulate cortex), inferior medial temporal, basal ganglia, precuneus and inferior parietal regions. These brain areas were associated with the cognitive and social cognition deficits characteristic of bvFTD 67–74. Similarly, this pattern of brain atrophy associated with TREM2 variants coincides with other reports 3.…”

“…These brain areas were associated with the cognitive and social cognition deficits characteristic of bvFTD. [67][68][69][70][71][72][73][74] Similarly, this pattern of brain atrophy associated with TREM2 variants coincides with other reports. 3 Moreover, similar relationships have been found in MRI studies of progressive non-fluent aphasia cases which suggest that the motor impairments are due to atrophy in regions within a left front-insular-basal ganglia network.…”

Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: a cross sectional observational study

“…Sequential Feature Selection algorithms were used as reported elsewhere 11 to reduce the space of d dimensional features to a subspace k, where k < d, to select the subset of features that were most predictive. This allowed us to remove irrelevant features, reducing generalization error and improving computational efficiency.…”

“…Our approach combined classical statistical methods (logistic regression models) and machine learning procedures 11 (support vector machine procedures, random forest, and sequential feature selection procedures) to identify the best factors to discriminate between AD, FTD, and HCs. Previous studies analysing dementia diagnosis using databases with high multidimensionality have revealed better accuracies with machine learning methods than with classical statistical models.…”

Editorial: Insights in Alzheimer's disease and related dementias

“…These landscapes of dementia science are changing rapidly, creating novel bridges across disciplines, diverse populations, regions, scales, methods and approaches. Some of these reconfigurations are driven by animal and human research focused in multiple emerging areas such as diversity contributions to genetic traits (Dehghani et al, 2021 ); heterogeneity and variation in protein misfolding and aggregation (Frisoni et al, 2022 ); explanatory models based on excitation/inhibition synaptic activity (Babiloni et al, 2020 ); impact of multiple sources of disparities (gender, admixtures, cultural, socioeconomic) (Alladi and Hachinski, 2018 ; Parra et al, 2018 , 2021 ); development of multimodal and region-specific biomarkers (Moguilner et al, 2022 ; Parra et al, 2022 ; Maito et al, 2023 ) and initiatives (Ibanez et al, 2021 ; Parra et al, 2021 ; Duran-Aniotz et al, 2022 ); interactions between environmental stressors and physiopathological mechanisms of allostatic overload (Birba et al, 2022 ; De Felice et al, 2022 ; Migeot et al, 2022 ); and going beyond universal models toward non-stereotypical samples (Greene et al, 2022 ) and designs (Ibanez, 2022 ) in neuroscience and dementia (Alladi and Hachinski, 2018 ). Notably, many of these key matters are being covered in this special issue.…”