The impact of physical exercise on hippocampal atrophy in mild cognitive impairment and Alzheimer's disease: a meta-analysis.

Physical activity (PA) is a promising therapeutic for Alzheimer's disease (AD). Only a handful of meta-analyses have studied the impact of PA interventions on regional brain volumes, and none to date has solely included studies on effect of PA on regional brain volumes in individuals with cognitive impairment (CI). In this meta-analysis, we examined whether there is support for the hypothesis that PA interventions positively impact hippocampal volume (HV) in individuals with CI. We also assessed whether the level of CI [mild CI (MCI) vs. AD] impacted this relationship. We identified six controlled trials that met inclusion criteria. These included 236 participants with AD, MCI, or preclinical AD. Data were extracted and analyzed following Cochrane guidelines. We used a random-effects model to estimate the mean change in HV pre- and post-exercise intervention. Forest plots, Hedges' g funnel plots, and Egger's test were used to assess unbiasedness and visualize intervention effects, and Tau 2 , Cochran's Q, and I 2 were calculated to assess heterogeneity. The primary analysis revealed a significant positive effect of PA on total HV. However, sub-group analyses indicated a significant preservation of HV only in individuals with MCI, but not in those with AD. Egger's test indicated no evidence of publication bias. Subgroup analyses also revealed significant heterogeneity only within the MCI cohort for the total and left HV. PA demonstrated a moderate, significant effect in preserving HV among individuals with MCI, but not AD, highlighting a therapeutic benefit, particularly in earlier disease stages.

Determining Accurate Dye Combinations for Sentinel Lymph Node Detection: A Systematic Review.

Background: Lymphatic dyes are commonly used to map the drainage path from tumor to lymphatics, which are biopsied to determine if spread has occurred. A blue dye in combination with technetium-99 is considered the gold standard for mapping, although many other dyes and dye combinations are used. Not all of these substances have the same detection efficacy. Methods: A systematic review of PubMed, SCOPUS, Web of Science, and Medline was performed. The predefined search terms were (indocyanine green OR isosulfan blue OR lymphazurin OR patent blue OR methylene blue OR fluorescein OR technetium-99) AND combination AND dye AND (sentinel lymph node biopsy OR lymphedema OR lymphatics OR lymph OR microsurgery OR cancer OR tumor OR melanoma OR carcinoma OR sarcoma). Results: The initial search returned 4267 articles. From these studies, 37 were selected as candidates that met inclusion criteria. After a full-text review, 34 studies were selected for inclusion. Eighty-nine methods of sentinel lymph node (SLN) detection were trialed using 22 unique dyes, dye combinations, or other tracers. In total, 12,157 SLNs of 12,801 SLNs were identified. Dye accuracy ranged from 100% to 69.8% detection. Five dye combinations had 100% accuracy. Dye combinations were more accurate than single dyes. Conclusions: Combining lymphatic dyes improves SLN detection results. Replacing technetium-99 with ICG may allow for increased access to SLN procedures with comparable results. The ideal SLN tracer is a low-cost molecule with a high affinity for lymphatic vessels due to size and chemical composition, visualization without specialized equipment, and no adverse effects.

Impact of Eating a Carbohydrate-Restricted Diet on Cortical Atrophy in a Cross-Section of Amyloid Positive Patients with Alzheimer's Disease: A Small Sample Study.

BACKGROUND: A carbohydrate-restricted diet aimed at lowering insulin levels has the potential to slow Alzheimer's disease (AD). Restricting carbohydrate consumption reduces insulin resistance, which could improve glucose uptake and neural health. A hallmark feature of AD is widespread cortical thinning; however, no study has demonstrated that lower net carbohydrate (nCHO) intake is linked to attenuated cortical atrophy in patients with AD and confirmed amyloidosis. OBJECTIVE: We tested the hypothesis that individuals with AD and confirmed amyloid burden eating a carbohydrate-restricted diet have thicker cortex than those eating a moderate-to-high carbohydrate diet. METHODS: A total of 31 patients (mean age 71.4±7.0 years) with AD and confirmed amyloid burden were divided into two groups based on a 130 g/day nCHO cutoff. Cortical thickness was estimated from T1-weighted MRI using FreeSurfer. Cortical surface analyses were corrected for multiple comparisons using cluster-wise probability. We assessed group differences using a two-tailed two-independent sample t-test. Linear regression analyses using nCHO as a continuous variable, accounting for confounders, were also conducted. RESULTS: The lower nCHO group had significantly thicker cortex within somatomotor and visual networks. Linear regression analysis revealed that lower nCHO intake levels had a significant association with cortical thickness within the frontoparietal, cingulo-opercular, and visual networks. CONCLUSIONS: Restricting carbohydrates may be associated with reduced atrophy in patients with AD. Lowering nCHO to under 130 g/day would allow patients to follow the well-validated MIND diet while benefiting from lower insulin levels.

Preliminary validation of a structural magnetic resonance imaging metric for tracking dementia-related neurodegeneration and future decline.

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and atrophy in the medial temporal lobe (MTL) and subsequent brain regions. Structural magnetic resonance imaging (sMRI) has been widely used in research and clinical care for diagnosis and monitoring AD progression. However, atrophy patterns are complex and vary by patient. To address this issue, researchers have made efforts to develop more concise metrics that can summarize AD-specific atrophy. Many of these methods can be difficult to interpret clinically, hampering adoption. In this study, we introduce a novel index which we call an "AD-NeuroScore," that uses a modified Euclidean-inspired distance function to calculate differences between regional brain volumes associated with cognitive decline. The index is adjusted for intracranial volume (ICV), age, sex, and scanner model. We validated AD-NeuroScore using 929 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, with a mean age of 72.7 years (SD = 6.3; 55.1-91.5) and cognitively normal (CN), mild cognitive impairment (MCI), or AD diagnoses. Our validation results showed that AD-NeuroScore was significantly associated with diagnosis and disease severity scores (measured by MMSE, CDR-SB, and ADAS-11) at baseline. Furthermore, baseline AD-NeuroScore was associated with both changes in diagnosis and disease severity scores at all time points with available data. The performance of AD-NeuroScore was equivalent or superior to adjusted hippocampal volume (AHV), a widely used metric in AD research. Further, AD-NeuroScore typically performed as well as or sometimes better when compared to other existing sMRI-based metrics. In conclusion, we have introduced a new metric, AD-NeuroScore, which shows promising results in detecting AD, benchmarking disease severity, and predicting disease progression. AD-NeuroScore differentiates itself from other metrics by being clinically practical and interpretable.

Utilizing machine learning algorithms to predict subject genetic mutation class from in silico models of neuronal networks.

BACKGROUND: Epilepsy is the fourth-most common neurological disorder, affecting an estimated 50 million patients globally. Nearly 40% of patients have uncontrolled seizures yet incur 80% of the cost. Anti-epileptic drugs commonly result in resistance and reversion to uncontrolled drug-resistant epilepsy and are often associated with significant adverse effects. This has led to a trial-and-error system in which physicians spend months to years attempting to identify the optimal therapeutic approach. OBJECTIVE: To investigate the potential clinical utility from the context of optimal therapeutic prediction of characterizing cellular electrophysiology. It is well-established that genomic data alone can sometimes be predictive of effective therapeutic approach. Thus, to assess the predictive power of electrophysiological data, machine learning strategies are implemented to predict a subject's genetically defined class in an in silico model using brief electrophysiological recordings obtained from simulated neuronal networks. METHODS: A dynamic network of isogenic neurons is modeled in silico for 1-s for 228 dynamically modeled patients falling into one of three categories: healthy, general sodium channel gain of function, or inhibitory sodium channel loss of function. Data from previous studies investigating the electrophysiological and cellular properties of neurons in vitro are used to define the parameters governing said models. Ninety-two electrophysiological features defining the nature and consistency of network connectivity, activity, waveform shape, and complexity are extracted for each patient network and t-tests are used for feature selection for the following machine learning algorithms: Neural Network, Support Vector Machine, Gaussian Naïve Bayes Classifier, Decision Tree, and Gradient Boosting Decision Tree. Finally, their performance in accurately predicting which genetic category the subjects fall under is assessed. RESULTS: Several machine learning algorithms excel in using electrophysiological data from isogenic neurons to accurately predict genetic class with a Gaussian Naïve Bayes Classifier predicting healthy, gain of function, and overall, with the best accuracy, area under the curve, and F1. The Gradient Boosting Decision Tree performs the best for loss of function models indicated by the same metrics. CONCLUSIONS: It is possible for machine learning algorithms to use electrophysiological data to predict clinically valuable metrics such as optimal therapeutic approach, especially when combining several models.