Translational Advances in Neurodegenerative Dementias, Second Edition

Background: attention-deficit/hyperactivity disorder (ADHD) and Alzheimer’s disease (AD) are distinct neurological conditions that may share genetic and molecular underpinnings. ADHD, a neurodevelopmental disorder, affects approximately 5% of children and 3% of adults globally, while AD, a neurodegenerative disorder, is the leading cause of dementia in older adults. Emerging evidence suggests potential overlapping contributors, including pathways related to synaptic plasticity, neuroinflammation, and oxidative stress. Methods: this systematic review investigated potential genetic predispositions linking Attention-Deficit/Hyperactivity Disorder (ADHD) and Alzheimer’s Disease (AD). Following PRISMA guidelines, a search was conducted in Web of Science, Embase, PsycINFO, and PubMed using keywords related to ADHD, AD, and genetic factors. Studies included were original human studies utilizing genetic analyses and ADHD polygenic risk scores (PRS), with AD confirmed using established diagnostic criteria. Exclusion criteria comprised non-original studies, animal research, and articles not addressing genetic links between ADHD and AD. Screening was conducted with Rayyan software, assessing relevance based on titles, abstracts, and full texts. Results:. The search identified 1450 records, of which 1092 were screened after duplicates were removed. Following exclusions, two studies met inclusion criteria. One study analyzed ADHD-PRS in 212 cognitively unimpaired older adults using amyloid-beta (Aβ) PET imaging and tau biomarkers. The findings revealed that ADHD-PRS was associated with progressive cognitive decline, increased tau pathology, and frontoparietal atrophy in Aβ-positive individuals, suggesting that ADHD genetic liability may exacerbate AD pathology. Another study assessed ADHD-PRS in a cohort of 10,645 Swedish twins, examining its association with 16 somatic conditions. The results showed modest risk increases for cardiometabolic, autoimmune, and neurological conditions, with mediation effects through BMI, education, tobacco use, and alcohol misuse, but no direct link between ADHD-PRS and dementia. Discussion and conclusion: this review highlights preliminary but conflicting evidence for a genetic intersection between ADHD and AD. One study suggests that ADHD genetic liability may exacerbate AD-related pathology in Aβ-positive individuals, whereas another large registry-based study finds no direct link to dementia, with associations largely mediated by lifestyle factors. The potential ADHD–AD relationship is likely complex and context-dependent, influenced by biomarker status and environmental confounders. Longitudinal studies integrating genetics, biomarkers, and detailed lifestyle data are needed to clarify this relationship. Full article

Objectives: This study investigated fractional anisotropy (FA) differences within key white matter tracts across patient groups stratified by Montreal Cognitive Assessment (MoCA) scores, aiming to evaluate FA’s potential as a biomarker for cognitive impairment. Methods: Seventy participants (aged 57–96 years) were categorized into high (HP, MoCA ≥ 26), moderate (MP, MoCA 18–25), and low (LP, MoCA < 18) cognitive performance groups. Diffusion Tensor Imaging (DTI) was used to obtain FA values in corticospinal tracts, superior longitudinal fasciculus, inferior fronto-occipital fasciculus, and cingulum. Statistical analyses included ANOVA and post-hoc tests. Results: Significant differences in FA values and normative percentiles were observed across cognitive groups in several tracts. Notably, the MP group exhibited significantly higher FA values in the Left Superior Longitudinal Fasciculus—Arcuate (mean FA 0.329 vs. LP 0.306, p = 0.033) and Right Superior Longitudinal Fasciculus—Arcuate (mean FA 0.329 vs. LP 0.306, p = 0.009), Left Inferior Fronto-Occipital Fasciculus (mean FA 0.308 vs. LP 0.283, p = 0.021), and Right Inferior Fronto-Occipital Fasciculus (mean FA 0.289 vs. LP 0.266, p = 0.017) compared to the LP group. Conclusions: Our findings reveal significant FA alterations across MoCA-defined cognitive groups, with moderate impairment showing higher FA than low performance. This suggests FA may reflect complex microstructural changes in early cognitive decline. While our modest sample size, particularly in the low-performance group, limits definitive conclusions, these results highlight the need for larger, multimodal studies to validate FA’s role as a sensitive, albeit complex, biomarker for cognitive impairment. Full article

Background/Objectives: Alzheimer’s disease (AD) is a progressively debilitating neurodegenerative disorder characterized by the accumulation of abnormal proteins, such as amyloid-beta plaques and tau tangles, leading to disruptions in memory storage and neuronal degeneration. Despite its portability, non-invasiveness, and cost-effectiveness, electroencephalography (EEG) as a diagnostic tool for AD faces challenges due to its susceptibility to noise and the complexity involved in the analysis. Methods: This study introduces a novel methodology employing three distinct stages for data-driven AD diagnosis: signal pre-processing, frame-level classification, and subject-level classification. At the frame level, convolutional neural networks (CNNs) are employed to extract features from spectrograms, scalograms, and Hilbert spectra. These features undergo fusion and are then fed into another CNN for feature selection and subsequent frame-level classification. After each frame for a subject is classified, a procedure is devised to determine if the subject has AD or not. Results: The proposed model demonstrates commendable performance, achieving over 80% accuracy, 82.5% sensitivity, and 81.3% specificity in distinguishing AD patients from healthy individuals at the subject level. Conclusions: This performance enables early and accurate diagnosis with significant clinical implications, offering substantial benefits over the existing methods through reduced misdiagnosis rates and improved patient outcomes, potentially revolutionizing AD screening and diagnostic practices. However, the model’s efficacy diminishes when presented with data from frontotemporal dementia (FTD) patients, emphasizing the need for further model refinement to address the intricate nuances associated with the simultaneous detection of various neurodegenerative disorders alongside AD. Full article