Alzheimer's Disease (AD) is the most common cause of demen:a, characterized by progressive accumula:on of amyloid-β plaques and tau protein neurofibrillary tangles, func:onal and structural dysconnec:vity and widespread cogni:ve impairment (Korczyn & Grinberg, 2024). Due to the mul:factorial nature of this disorder, no effec:ve individual pharmaceu:cal treatments have yet been iden:fied (Korczyn & Grinberg, 2024). Thus, research has become increasingly focused on iden:fying early biomarkers of pathology that can predict disease progression and guide interven:ons to preserve brain health. Mild cogni:ve impairment (MCI) is the prodromal stage of AD and is characterized by cogni:ve impairment beyond what is expected for one's age or educa:on level (Nasreddine et al., 2005). Not all individuals who experience MCI symptoms will convert to AD (Farias et al., 2009), making it a promising window for interven:on to prevent disease progression. Cri:cally, the heterogeneity of MCI condi:ons has limited the ability to pinpoint a single conversion mechanism. Using advancements in technology, it is possible to incorporate mul:-factor models that can provide more wholis:c evalua:ons of this mul:faceted condi:on.Succeeding in iden:fying early biomarkers of pathology and in modeling the disease progression are two building blocks necessary for the development of successful interven:onal therapies. Nonpharmacological interven:ons for boos:ng residual cogni:ve func:ons, such as noninvasive neuros:mula:on, have grown in popularity due to their limited side effects and flexibility to adapt protocols for individualized therapy. However, the op:mal targets and s:mula:on parameters needed to elicit longer term cogni:ve benefits are yet to be iden:fied. Cor:cal atrophy represents a hallmark of aging, characterized by volume loss at the whole brain level (Raji et al., 2009). In older individuals developing AD pathology, such atrophic pa2erns appear exacerbated over hippocampal and entorhinal cortex regions (Peng et al., 2015;Raji et al., 2009), resul:ng in the cogni:ve deficits that eventually result in the need for clinical a2en:on, i.e. episodic memory failure. More recently, the call towards the iden:fica:on of early biomarkers of AD has guided inves:ga:ons towards iden:fying pa2erns of atrophy occurring well before any formal diagnosis. In this regard, Hu et al inves:gated the discriminatory power between volumetric loss due to normal aging versus AD-specific pathology. Using retrospec:ve longitudinal analysis, they iden:fied that greater levels of atrophy across the frontal and temporal lobes preceded the onset of AD, which was exacerbated in EPOE ε4 muta:on carriers.However, volumetric altera:ons do not happen in isola:on, but are oaen accompanied by cerebrovascular changes, such as white ma2er hyperintensi:es (WMH; Hu et al., 2021). Given the :ght link between vascular pathology and neurodegenera:on, Cao et al. iden:fied that greater WMH volumes was associated with greater atrophy of specific brain regions within the temporal lobe and insular. The rela:onship between vascular lesions, atrophy and neurodegenera:on lends support for the need to promote lifestyle interven:ons known to modify these risk factors (e.g., obesity, hypertension, diabetes, smoking and physical inac:vity) (Livingston et al., 2017). Given the mul:factorial nature of AD, models should consider clinical, biological, molecular, gene:c and neuropsychological variables in combina:on. By combining informa:on from cogni:ve tests, cor:cal thickness, and amyloid-β positron emission tomography (PET) scans, Jung et al. developed a deep learning model with high predictability of cogni:ve and structural altera:ons over :me in MCI pa:ents. Jiang et al also evaluated the goodness of several machine learning models for discrimina:ng between cogni:vely normal and AD par:cipants. Interes:ngly, in their approach the authors use cor:cal complexity, described as the fractal dimension of the cor:cal surface, as a more sensi:ve measure of atrophy than tradi:onal volumetric analyses (Nicastro et al., 2020). Overall, the use of machine learning is becoming increasingly popular given the ability of such models to deal with high dimensional, nonlinear data necessary to output individual-centered predic:ons.Animal models provide an alterna:ve approach for inves:ga:ng the pathological mechanisms of AD. Indeed, the shorter lifespan (e.g. in rat models) allows for monitoring the evolu:on of the disease phenotype in a rela:vely brief period and with the possibility to directly exert control on the experimental variables. De Waegenaere and colleagues iden:fied changes in brain states between the pre-and early-plaque stages in rat models that involved networks equivalent to the default mode network, lateral cor:cal network, and basal forebrain regions in humans.Both machine learning and animal models provide important opportuni:es to determine the pathological features of AD and can help us characterize the early impact and biomarkers of disease progression. In this special issue, Xu et al. report on the beneficial effects of Moxibus:on treatment for increased func:onal connec:vity and cogni:ve func:oning, whereas Kim and colleagues present a study protocol for the implementa:on of a personalized interven:on using transcranial direct current s:mula:on. The recent evolu:on in this field arises from the necessity to overcome the limita:ons of "tradi:onal symptom-and sign-based diagnoses and clinical one-size-fits-all interven:ons", which have long failed in heterogeneous diseases such as AD (Hampel et al., 2019). However, the vast array of neuromodula:on techniques, sites of s:mula:on and criteria for inclusion require greater refinement. Among the possible ameliora:ve interven:ons, there is the need for personalized protocols based on individual head models (Menardi et al., 2022). Informa:on on the individual neuroanatomy can be used to predict the amount of electrical current reaching the targeted area and further implement searching algorithms for the op:miza:on of electrodes/coil loca:ons on the scalp (Gomez et al., 2021;Miranda et al., 2018). AD represents a complex and mul:factorial disease for which no cure is available. Precision Medicine approaches call for individually tailored therapeu:c modali:es based on the mul:modal integra:on of the pa:ent's data to establish preven:ve, predic:ve, personalized and par:cipatory interven:ons (Hampel et al., 2023). Current efforts revolve around the: i) defini:on of early biomarkers of pathology to iden:fy individuals at risk, ii) modeling of the disease course to predict pathological progression, and iii) defining pa:ent-centered therapies to increase resilience and help counteract pathological progression.Author contribu?ons AM: Conceptualiza:on, Wri:ng-original draa. RC: Wri:ng-review & edi:ng.