Search Results (12,005)

Background: Population aging is accompanied by progressive immune remodeling, chronic low-grade inflammation, and increased susceptibility to neurodegenerative diseases. Although the microbiota–gut–brain axis is increasingly recognized as a regulator of neuroimmune homeostasis, mechanisms linking age-associated dysbiosis with immunosenescence, barrier dysfunction, and brain aging remain incompletely understood. This review integrates current evidence into the proposed Gut–Immune–Brain Resilience Axis (GIBRA), a systems-level model describing how microbial signaling shapes neuroimmune resilience during aging. Methods: A structured narrative review was conducted using PubMed and the Web of Science Core Collection from database inception up to July 2026. Evidence from systematic reviews, meta-analyses, consensus statements, mechanistic and translational studies, longitudinal cohorts, randomized clinical trials, and observational studies was synthesized. Results: Current evidence supports an important role for the disruption of microbial functional signaling in neuroimmune aging, while microbial taxonomy and functional profiles provide complementary levels of biological information. Reduced short-chain fatty acid production, dysregulated tryptophan metabolism, microbial translocation, endotoxin-mediated innate immune activation, and gut-conditioned adaptive immune responses promote immunosenescence, inflammaging, barrier dysfunction, and microglial priming. The proposed Double-Barrier Hypothesis links intestinal and blood–brain barrier dysfunction as complementary mechanisms underlying chronic neuroinflammation. GIBRA highlights functional microbiome endotypes, biomarkers, multi-omics, and artificial intelligence as emerging tools for precision medicine. Conclusions: GIBRA provides an integrated systems biology perspective connecting microbial signaling, immune resilience, barrier integrity, and brain resilience during aging. Prioritizing functional resilience over microbial taxonomy may improve biomarker discovery, patient stratification, and microbiome-targeted interventions for neurodegenerative disease prevention. Prospective longitudinal studies integrating multi-omics are needed to support clinical translation. Full article

Background and Objectives: Alzheimer’s disease (AD) is the most common neurodegenerative disorder, and current therapies provide only limited symptomatic relief without effectively slowing its progression. Increasing evidence suggests that aberrant activation of the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling cascade contributes to AD-associated neuroinflammation. This study investigated the therapeutic potential and molecular mechanisms of JAK inhibitors in AD using integrated bioinformatics and network pharmacology approaches. Materials and Methods: Potential anti-AD targets of JAK inhibitors were identified using the SwissTargetPrediction and GeneCards databases. Functional enrichment, protein–protein interaction (PPI) analysis, transcriptomic validation using public datasets, regulatory network construction, molecular docking, normal mode analysis (NMA), and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction were performed to investigate the potential mechanisms of action of these drugs in AD. Results: Our analysis identified 163 shared targets between JAK inhibitors and AD. Enrichment analysis revealed that these genes were primarily involved in protein phosphorylation and were enriched in key signaling pathways, including the neurotrophin, phosphoinositide 3-kinase/protein kinase B (PI3K/Akt), and mitogen-activated protein kinase (MAPK) signaling pathways. PPI analysis identified AKT1, BCL2, SRC, STAT3, and TNF as five highly ranked hub targets across multiple topological algorithms. Transcriptomic validation confirmed significantly higher expression of these targets in the prefrontal cortex of individuals with AD compared with normal subjects. Molecular docking indicated that pacritinib and momelotinib showed relatively favorable predicted interactions with the hub proteins, while NMA revealed differences in the predicted flexibility of the docked complexes. Furthermore, ADMET prediction showed that pacritinib possesses favorable pharmacokinetic properties for the treatment of AD. Conclusions: Collectively, these findings provide mechanistic insights into the potential effects of JAK inhibitors in AD and identify pacritinib as a computationally prioritized candidate that warrants experimental validation in appropriate AD models. However, as this study is based solely on computational analyses without wet-lab validation, the findings should be considered hypothesis-generating in silico evidence, and the potential safety concerns of pacritinib require further investigation. Full article

Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are neurodegenerative disorders with partially overlapping clinical manifestations, making early and differential diagnosis challenging. This study investigated whether electroencephalography (EEG)-derived spectral features and Granger-causality (GC)-based directed functional connectivity features can characterize and classify AD, FTD, and healthy control (HC) subjects. Resting-state eyes-closed EEG recordings from 88 participants were analyzed, including 36 AD, 23 FTD, and 29 HC subjects. Spectral features included absolute and relative band power and spectral ratios, while directed connectivity features were extracted from broadband and frequency-specific GC matrices. Statistical analyses identified theta/alpha ratio (TAR) as the dominant spectral marker, with the strongest three-group differences observed in frontal and global TAR features. GC analysis revealed group-related alterations mainly in alpha-band regional directed connectivity, although three-group GC features did not survive false discovery rate (FDR) correction at q < 0.05. In the main nested cross-validation analysis, the spectral-only model achieved the best three-class performance, with balanced accuracy of 0.572 and macro-F1 of 0.557. For dementia group (DEM) vs. HC classification, the combined GC + spectral feature (GC + SPEC) set achieved balanced accuracy of 0.710 and macro-F1 of 0.665. For AD vs. FTD classification, the combined GC + SPEC feature set achieved the highest numerical performance in the main nested cross-validation (CV) comparison, with balanced accuracy of 0.584 and macro-F1 of 0.559. In the separate long permutation-testing analysis, which used a reduced hyperparameter grid for computational feasibility, above-chance performance was confirmed for the three-class spectral model and the DEM vs. HC GC + SPEC model (p < 0.001), but not for AD vs. FTD (p = 0.270). An exploratory photobiomodulation (PBM) single-case analysis showed longitudinal EEG reorganization, including increased alpha power, reduced delta/alpha ratio (DAR) and beta/alpha ratio (BAR), mixed TAR changes, and HC-like GC/GC + SPEC centroid projections. Overall, the results support the value of spectral and directed connectivity EEG markers for dementia-related EEG characterization, while highlighting the persistent difficulty of AD vs. FTD differentiation. Full article

Neurological disorders like Alzheimer’s disease, Parkinson’s disease, and epilepsy are becoming major causes of disability and mortality worldwide, and their prevalence is expected to rapidly increase with the aging of the population. These diseases develop silently, with irreversible neuronal damage often occurring decades before any clinical signs of illness are noticed, making early diagnosis and treatment difficult. The presymptomatic period greatly restricts the effectiveness of therapeutic interventions and reduces the possibility of disease-modifying interventions. Traditional diagnostic methods based on clinical assessment, neuroimaging, and invasive biomarkers are not sensitive enough to identify the disease at an early stage and are expensive to the healthcare system. The latest artificial intelligence (AI) technology and machine learning (ML) approaches, together with digital biomarkers obtained from eye tracking, facial expressions, speech analysis, motor dynamics, electrophysiology, wearable devices, and passive sensing, offer promising non-invasive alternatives for early detection of diseases. However, most reported performance metrics are derived from retrospective or pre-validated datasets, and prospective external validation remains limited. This narrative review synthesizes current evidence on AI-driven digital biomarkers for early detection of neurological diseases, examining disease-specific applications, methodological approaches, and challenges in clinical practices. We emphasize that clinical utility is task specific and dependent on disease stage, validation design, clinical endpoints, cost, workflow integration, and availability of disease-modifying therapies. We also note that much of the evidence summarized here derives from retrospective, case-control, or internally validated datasets and that prospective, patient-independent, and external validation with clinically meaningful endpoints remains limited. Reported performance figures should be read as proof-of-concept evidence rather than as evidence of demonstrated clinical readiness. We highlight promising future directions, including federated learning, explainable AI, and precision neurology approaches, while acknowledging that most applications remain investigational and require prospective validation before broad clinical deployment. Full article

Background: Intracellular Ca²⁺ dysregulation and neurovascular dysfunction are increasingly recognized as important mechanisms underlying neurodegenerative disorders, including Alzheimer’s disease (AD). Restoration of pathological Ca²⁺ imbalance has therefore emerged as a potential therapeutic strategy. Although ginsenoside compound K (CK) has demonstrated neuroprotective and anti-inflammatory properties, its role in Ca²⁺ homeostasis remains unclear. Methods: SH-SY5Y neuronal cells, U373 astrocytes, BV2 microglia, bEND brain endothelial cells, and MOVAS vascular smooth muscle cells were exposed to trimethyltin chloride (TMT, 5 μM, 24 h). Cell viability, real-time cell confluence, intracellular Ca²⁺ influx, and endoplasmic reticulum (ER) Ca²⁺ store release were evaluated. Ca²⁺ dynamics were analyzed using Fura-2 fluorescence, and signaling associated with CK-mediated Ca²⁺ regulation was investigated using pharmacological inhibitors. Orai1 protein expression was evaluated by Western blot analysis in SH-SY5Y and U373 cells. Results: TMT increased store-operated Ca²⁺ entry (SOCE)-mediated Ca²⁺ influx in SH-SY5Y neuronal cells without significant cytotoxicity. In contrast, TMT reduced both Ca²⁺ influx and cell viability in U373 astrocytes, BV2 microglia, and MOVAS cells, whereas bEND cells showed minimal changes. CK restored abnormal Ca²⁺ responses in a cell type-specific manner, reducing elevated Ca²⁺ influx in neuronal cells while restoring suppressed Ca²⁺ signaling in astrocytes. Consistent with these functional findings, Orai1 protein expression was increased by TMT in SH-SY5Y cells and attenuated by CK, whereas TMT reduced Orai1 expression in U373 cells, which was restored by CK. Pharmacological analyses suggested involvement of PKA- and LTCC-associated signaling in neuronal cells and PLC- and PLD-associated signaling in astrocytes. In both cell types, CK-associated Ca²⁺ responses were sensitive to NAC and the SOCE inhibitor BTP2, supporting ROS- and SOCE-associated mechanisms. Additional controls showed no significant inhibitor effects under control or CK-only conditions, strengthening the pharmacological interpretation of responses under TMT-containing conditions. Conclusions: TMT-induced Ca²⁺ dysregulation differed markedly by cell type. CK restored disrupted Ca²⁺ homeostasis in association with distinct pharmacological signaling profiles in neuronal and astrocytic cells. These findings suggest that CK may function as a cell type-specific Ca²⁺ homeostatic regulator under neurotoxic stress conditions and highlight the importance of differential Ca²⁺ regulation in neurodegenerative disease models. Full article

Alzheimer’s disease and related dementias are projected to affect more than 150 million people worldwide by 2050. Early staging with validated instruments such as the Clinical Dementia Rating (CDR) scale is essential for timely intervention, yet access to clinician-administered CDR assessment remains constrained by workforce, time, and geographic barriers. This study complements a previously published machine learning pipeline for Alzheimer’s disease prediction by addressing the downstream task of dementia staging. Because the global CDR score is already derived from the six sub-domain ratings through an established rule-based procedure, the contribution reported here lies not in discovering that mapping but in encoding it in a transparent, deployable form: an explainable decision tree classifier embedded in DiAbot, a large-language-model-fronted conversational system that supports self-administered CDR-style assessment. We extracted 13,453 CDR records from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), removed administrative variables, invalid entries, and missing rows (final n = 13,290), and trained decision tree classifiers under two impurity criteria, Information Gain and Gini Index, using a 70/30 stratified record-level hold-out and ten-fold stratified record-level cross-validation. This classifier-level evaluation uses the six domain scores as recorded during ADNI’s clinician-administered assessment, not scores elicited by the DiAbot chatbot; the trained classifier was separately embedded in a web application in which a prompt-engineered large language model conducts a CDR-style interview and normalizes responses to ordinal domain scores, but the end-to-end accuracy of that full conversational pipeline (chatbot elicitation through to final CDGLOBAL) has not yet been measured, and is not what the headline accuracy figures below report. The Information Gain Decision Tree reproduced the established mapping from the six CDR sub-domain scores to the CDGLOBAL with 99.86% accuracy under the record-level hold-out protocol (matching macro-averaged precision, recall, and F1-score), with a ten-fold record-level cross-validated mean of 99.81% (SD 0.07); this result represents fidelity to the established CDR scoring rule rather than independent dementia-diagnosis accuracy. Gini-based trees performed almost identically (99.79% hold-out, 99.74% cross-validated). Memory dominated feature importance, consistent with its role as the primary domain in the official CDR scoring algorithm. Residual misclassifications were confined to adjacent CDR stages. Because the CDGLOBAL is deterministically derived from the six sub-domain scores, these figures should be read throughout as evidence of high-fidelity reconstruction of the established CDR scoring relationship, not as general dementia-diagnosis accuracy comparable to imaging- or biomarker-based classifiers; further, participant-independent generalization remains unverified under the record-level protocol evaluated here. An interpretable classifier embedded in a conversational front-end can nonetheless make standardized CDR-style staging more widely accessible while preserving clinical inspectability; the resulting system is positioned as a screening-stage adjunct to, and not a replacement for, clinician-administered CDR assessment. Full article

Hypoxia-inducible factors (HIF-1α, HIF-2α, HIF-3α) regulate cellular adaptation to oxygen deprivation, but their region-specific roles in Alzheimer’s disease (AD) and AD with cerebral amyloid angiopathy (CAA) remain unclear. Using post-mortem human brain tissue from aging, AD, and AD + CAA groups, we measured all three HIF isoforms in hippocampal subfields (CA1, CA2, CA4, dentate gyrus) and anterior cingulate cortex (ACC) layers 3 and 5. In the AD hippocampus, two distinct patterns emerged: ischemia-resistant regions (CA4, DG) maintained HIF-2α and showed relative resilience, whereas vulnerable regions (CA1, CA2) exhibited HIF-1α upregulation, HIF-3α loss, and HIF-2α dysregulation. The ACC contrasts sharply with the hippocampus by preserving coordinated HIF-1α/HIF-3α regulation during aging and AD, with layer-specific divergence (exhaustion in layer 3 vs. resilience in layer 5) emerging only upon addition of CAA. Notably, HIF-2α in ACC neurons remains stably elevated across all conditions. Taken together, our results highlight HIF-2α as a potential contributor to regional vulnerability and raise the possibility that maintaining HIF-2α levels, in addition to or instead of modulating HIF-1α, could be worthy of further investigation in the context of AD and related vascular changes. Full article

Background/Objectives: Self-awareness constitutes a key metacognitive construct supporting self-regulation and adaptive functioning in aging. This systematic review examined how self-awareness fluctuates across the Alzheimer’s disease (AD) continuum and explored its associations with cognitive performance and neuroimaging markers. Methods: A systematic search was conducted in databases including PubMed, Scopus, Science Direct and Web of Science covering the period from 2016 to 2026, and the review was registered on the Open Science Framework (OSF). The selection process followed PRISMA guidelines, and a total of 334 studies were screened for eligibility while 42 met the inclusion criteria. Studies were eligible if they examined self-awareness in relation to cognitive and/or neuroimaging parameters, with individuals in the preclinical and clinical spectrum of AD as the reference population. Results: The included studies highlighted self-awareness as a dynamic construct closely linked to cognitive performance and neural integrity, with measurable deviations emerging along the continuum from subjective cognitive decline to dementia. Accordingly, the findings suggest that alterations in self-awareness may reflect the stage-dependent cognitive and neurobiological changes that characterize the progression of AD. Conclusions: Converging evidence suggests that assessing fluctuations of self-awareness, ranging from heightened awareness to reduced awareness, may contribute to the early identification of individuals at risk of progression across the AD continuum. However, the substantial methodological heterogeneity across studies precludes definitive conclusions regarding its clinical utility. Future longitudinal studies employing standardized assessment protocols are needed to determine whether these changes can reliably predict disease progression. Full article

Alzheimer’s disease (AD) is the leading cause of dementia and a major cause of death worldwide, making early detection a critical clinical priority. Because pathological changes may begin 15–20 years before symptom onset, artificial intelligence (AI) has emerged as a promising tool for identifying and characterizing AD. In particular, multi-modal approaches that integrate cognitive, biological, and sensor-based data have attracted growing interest. This systematic review compares single- and multi-modal AI strategies for AD detection, covering machine learning and deep learning methods, feature representations, validation strategies, and classification tasks. Searches of major databases identified 568 studies published between 2016 and early 2026; 278 met the inclusion criteria according to PRISMA guidelines. Multi-modal approaches generally achieved higher performance than single-modal strategies, particularly for challenging tasks such as predicting progression between closely related disease stages, although direct comparisons under identical conditions remain scarce. Critically, only about 4% of studies evaluated their models on a genuinely independent external cohort, raising substantial concerns about model generalizability. Overall, current AI systems remain highly dependent on existing datasets and heterogeneous evaluation protocols, which limit generalizability and clinical applicability. Future research should prioritize representative multimodal datasets, rigorous external validation, and clinically interpretable AI systems. Full article

We conducted a systematic review and meta-analysis of associations between apolipoprotein E (APOE) alleles and frontotemporal lobar degeneration (FTLD)-spectrum disorders. MEDLINE, Embase, CENTRAL, and Google Scholar were searched. APOE2 and APOE4 carrier status were compared between FTLD-spectrum disorders and healthy controls (HCs) or individuals with Alzheimer’s disease (AD). Forty studies were included. APOE4 carriage was more frequent in frontotemporal dementia (FTD) compared with HC (OR = 1.72; 95%CI = 1.45–2.04) and less common than in AD (OR = 0.35; 95%CI = 0.29–0.42). In contrast, APOE2 carriage was less prevalent in FTD relative to HC (OR = 0.83; 95%CI = 0.70–0.98) but more frequent compared with AD (OR = 1.80; 95%CI = 1.29–2.52). APOE4 effects were most pronounced in behavioral variant FTD. In clinically confirmed progressive supranuclear palsy (PSP), APOE4 carriage was not associated with PSP. Analysis restricted to pathologically confirmed PSP cases, however, showed lower APOE4 carriage in PSP than in healthy controls (OR = 0.78, 95% CI = 0.65–0.94), although this association failed to reach the multiplicity-adjusted significance threshold. APOE2 carriage was not associated with PSP in either clinically established or pathologically confirmed samples. Evidence was insufficient to establish or exclude associations for other FTLD-spectrum disorders because of the limited available data. In conclusion, APOE alleles show distinct associations across the FTLD spectrum. Full article

Nitrogen-containing fused tetracyclic systems, exemplified by the synthetic 11H-indeno[1,2-b]quinoxalin-11-one core and the natural alkaloid tryptanthrin (indolo[2,1-b]quinazolin-6,12-dione), constitute structural scaffolds whose rigid, planar architecture enables high-affinity interaction with nucleic acids and kinase active sites. Converting the exocyclic carbonyls at C-11 and C-6, respectively, into oximes has become a productive strategy in medicinal chemistry. This transformation modulates frontier orbital energies, installs N,O- and N,N-chelating pharmacophores, and enables nitric oxide (NO) release. Here, we summarize current knowledge of the synthesis, stereochemical characterization, and diverse biological activities of these tetracyclic ketoximes and related derivatives. Microwave, sonochemical, visible-light photocatalytic, and multicomponent methods now afford efficient, economical routes to the parent ketones and their oximes. X-ray crystallography, spectroscopy, and density functional theory have firmly established the thermodynamic preference for the E-oxime configuration and clarified how this geometry, along with potential target-induced isomerization, shapes binding. The oximes bind c-Jun N-terminal kinases (JNK1–3) with high affinity, a property that accounts for their neuroprotective effects in models of cerebral ischemia and Alzheimer-like pathology, their dual JNK inhibition and NO-mediated cardioprotection in hypertension and myocardial infarction, and their anti-inflammatory activity via suppression of NF-κB/AP-1 signaling. Broader studies also document anticancer, antimicrobial, antiviral, and antidiabetic activities arising from DNA intercalation, topoisomerase inhibition, metal-ion coordination, and kinase blockade. Compelling preclinical profiles notwithstanding, low oral bioavailability and rapid hepatic clearance remain major pharmacokinetic obstacles. Ongoing work on new formulations, prodrug strategies, and structure–activity optimization seeks to slow systemic elimination. Precise stereochemical definition combined with pleiotropic pharmacology positions tetracyclic ketoximes as attractive candidates for next-generation agents against complex multifactorial diseases. Full article

Mitochondrial dysfunction, including impaired respiration and increased reactive oxygen species (ROS), is an early feature of Alzheimer’s disease (AD) models. Electron transport chain supercomplexes (mSCs) regulate respiratory efficiency and ROS generation, yet genetic determinants of mSC organization in AD models remain underappreciated. Cox7a2l is known to promote CIII₂/CIV association and CIV incorporation into mSCs. We examined the commonly used mixed-background B6;129S 3xTg-AD mice and B6129SF2/J controls aged 12–15 months, as well as specific rat AD models for changes in mSC organization using blue and clear native-PAGE and DIA-MS. BN and CN-PAGE revealed a marked shift toward larger mSC assemblies in 3xTg-AD mice and a distinct ~650 kDa assembly of complex III absent from controls. Gene sequencing showed that 3xTg-AD mice retained the full-length Cox7a2l variant similar to 129S strains, whereas controls predominantly carried a shortened C57BL/6-associated variant. DIA-MS identified greatly increased Cox7a2l in differential mSC bands. Similar changes were found in 3xTg-AD heart tissue. In contrast, AD rat models and their controls, all expressing full-length Cox7a2l, did not exhibit comparable mSC differences. These findings suggest Cox7a2l genotype as a determinant of mSC organization and highlight the need to account for genetic background when interpreting mSC-dependent mitochondrial phenotypes in mixed-background 3xTg-AD studies. This distinction is essential for accurately separating strain-dependent effects from AD-associated mitochondrial changes. Full article

Background/Objectives: Evaluating noninvasive, accessible ocular and blood-based biomarkers could aid in early risk stratification and disease detection during the preclinical phase of Alzheimer’s disease. This study investigates associations between plasma biomarkers of neurodegeneration and retinal structural and microvasculature parameters in cognitively normal adults with familial or genetic risk factors for Alzheimer’s disease. Methods: Forty-one participants underwent plasma sampling for glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), amyloid-beta42 (β42), amyloid-β42/40 ratio, and phosphorylated-tau217 (p-tau217), and also underwent optical coherence tomography (OCT) and OCT angiography (OCTA) imaging. Apolipoprotein E genotyping and family history of Alzheimer’s disease were recorded. Generalized estimating equations adjusting for age, sex, race, treated hypertension, Alzheimer’s disease family history, and APOE ε4 carrier status assessed associations between plasma biomarker concentrations and OCT and OCTA measurements. Results: Higher plasma GFAP and NfL concentrations were significantly associated with reduced superficial capillary plexus perfusion density and vessel density on macular OCTA. In additional analyses restricted to participants with plasma biomarker and retinal imaging assessments obtained within 9 months, associations with GFAP remained significant, whereas NfL associations no longer remained significant after correction for multiple comparisons. Conclusions: Retinal OCTA and OCT metrics may reflect general neurovascular aging in cognitively normal individuals with familial or genetic risk factors for Alzheimer’s disease. Full article

Dysregulated insulin signaling in the brain has been linked to cognitive impairment and dementia. Insulin-like growth factor 1 (IGF-1) is a peptide growth hormone crucial for neurogenesis and neuroprotection. Findings regarding potential involvement of IGF-1 in dementia have been conflicting, and the status of IGF-1 in clinical cohorts with Alzheimer’s disease (AD) and concomitant cerebrovascular disease (CeVD) burden is unknown. A Singapore-based memory clinic cohort consisting of 46 non-cognitively impaired (NCI), 101 with cognitive impairment, no dementia (CIND) and 81 AD dementia subjects underwent plasma IGF-1 measurements and neuroimaging assessments for association analyses of peripheral IGF-1 with regional brain volumes, as well as with neuroimaging CeVD markers (lacunes, cerebral microbleeds, white matter hyperintensities). Plasma IGF-1 levels were significantly lower in AD compared to NCI and CIND participants (both p < 0.001). Plasma IGF-1 was significantly associated with smaller hippocampal (p = 0.035), amygdala (p = 0.024), parietal lobe (p = 0.029), and frontal lobe (p = 0.002) volumes. In contrast, plasma IGF-1 did not associate with CeVD markers after covariate adjustments. Our findings suggest that plasma IGF-1 may be a blood-based biomarker for reduced brain volumes, while having no direct role in CeVD pathophysiology. Full article

The Olfactory System is receiving increasing attention in recent years as a potential biomarker for Alzheimer’s disease (AD). Early-stage AD is often associated with a decline in olfactory function, with studies suggesting that olfactory memory deficit may precede cognitive symptoms. We explore the intricate relationship between the Olfactory System and Alzheimer’s disease, examining both the neuroanatomical and physiological changes that occur in the olfactory pathways during the progression of AD. Memory, as a functional feature, was modeled using artificial neural networks, and it was related to the macro-parameters of the network. While these networks cannot completely capture the intricacies and functions of the human brain, they provide a clear understanding of how processes occur within it. Neural networks exhibited memory domains, defined by stable and unstable steady states; the former can be considered a prerequisite for memory storage and recall; the latter can be considered threshold values between stable steady states. Additionally, by introducing division of the neural population into subpopulations, the networks manifested multiple stable states, corresponding to multiple memory domains, which in a qualitative manner suggest hierarchically organized nonlinear complexity and multi-scale self-similarity of memory processes. Full article