Sensorimotor Cortex Reorganization in Alzheimer's Disease and Metal Dysfunction: A MEG Study

Progressive cervical cord atrophy parallels cognitive decline in Alzheimer's disease

Alzheimer's disease (AD) is characterized by progressive episodic memory dysfunction. A prominent hallmark of AD is gradual brain atrophy. Despite extensive research on brain pathology, the understanding of spinal cord pathology in AD and its association with cognitive decline remains understudied. We analyzed serial magnetic resonance imaging (MRI) scans from the ADNI data repository to assess whether progressive cord atrophy is associated with clinical worsening. Cervical cord morphometry was measured in 45 patients and 49 cognitively normal controls (CN) at two time points over 1.5 years. Regression analysis examined associations between cord atrophy rate and cognitive worsening. Cognitive and functional activity performance declined in patients during follow-up. Compared with controls, patients showed a greater rate of decline of the anterior-posterior width of the cross-sectional cord area per month (- 0.12%, p = 0.036). Worsening in the mini-mental state examination (MMSE), clinical dementia rating (CDR), and functional assessment questionnaire (FAQ) was associated with faster rates of cord atrophy (MMSE: r = 0.320, p = 0.037; CDR: r = - 0.361, p = 0.017; FAQ: r = - 0.398, p = 0.029). Progressive cord atrophy occurs in AD patients; its rate over time being associated with cognitive and functional activity decline.

Correlation transfer function analysis as a biomarker for Alzheimer brain plasticity using longitudinal resting-state fMRI data

Neural plasticity is the ability of the brain to alter itself functionally and structurally as a result of its experience. However, longitudinal changes in functional connectivity of the brain are still unrevealed in Alzheimer's disease (AD). This study aims to discover the significant connections (SCs) between brain regions for AD stages longitudinally using correlation transfer function (CorrTF) as a new biomarker for the disease progression. The dataset consists of: 29 normal controls (NC), and 23, 24, and 23 for early, late mild cognitive impairments (EMCI, LMCI), and ADs, respectively, along three distant visits. The brain was divided into 116 regions using the automated anatomical labeling atlas, where the intensity time series is calculated, and the CorrTF connections are extracted for each region. Finally, the standard t-test and ANOVA test were employed to investigate the SCs for each subject's visit. No SCs, along three visits, were found For NC subjects. The most SCs were mainly directed from cerebellum in case of EMCI and LMCI. Furthermore, the hippocampus connectivity increased in LMCI compared to EMCI whereas missed in AD. Additionally, the patterns of longitudinal changes among the different AD stages compared to Pearson Correlation were similar, for SMC, VC, DMN, and Cereb networks, while differed for EAN and SN networks. Our findings define how brain changes over time, which could help detect functional changes linked to each AD stage and better understand the disease behavior.

The Role of Zinc in the Treatment of Wilson’s Disease

Wilson’s disease (WD) is a hereditary disorder of copper metabolism, producing abnormally high levels of non-ceruloplasmin-bound copper, the determinant of the pathogenic process causing brain and hepatic damage and dysfunction. Although the disease is invariably fatal without medication, it is treatable and many of its adverse effects are reversible. Diagnosis is difficult due to the large range and severity of symptoms. A high index of suspicion is required as patients may have only a few of the many possible biomarkers. The genetic prevalence of ATP7B variants indicates higher rates in the population than are currently diagnosed. Treatments have evolved from chelators that reduce stored copper to zinc, which reduces the toxic levels of circulating non-ceruloplasmin-bound copper. Zinc induces intestinal metallothionein, which blocks copper absorption and increases excretion in the stools, resulting in an improvement in symptoms. Two meta-analyses and several large retrospective studies indicate that zinc is equally effective as chelators for the treatment of WD, with the advantages of a very low level of toxicity and only the minor side effect of gastric disturbance. Zinc is recommended as a first-line treatment for neurological presentations and is gaining acceptance for hepatic presentations. It is universally recommended for lifelong maintenance therapy and for presymptomatic WD.

Alzheimer disease stages identification based on correlation transfer function system using resting-state functional magnetic resonance imaging

Alzheimer’s disease (AD) affects the quality of life as it causes; memory loss, difficulty in thinking, learning, and performing familiar tasks. Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to investigate and analyze different brain regions for AD identification. This study investigates the effectiveness of using correlated transfer function (CorrTF) as a new biomarker to extract the essential features from rs-fMRI, along with support vector machine (SVM) ordered hierarchically, in order to distinguish between the different AD stages. Additionally, we explored the regions, showing significant changes based on the CorrTF extracted features’ strength among different AD stages. First, the process was initialized by applying the preprocessing on rs-fMRI data samples to reduce noise and retain the essential information. Then, the automated anatomical labeling (AAL) atlas was employed to divide the brain into 116 regions, where the intensity time series was calculated, and the CorrTF features were extracted for each region. The proposed framework employed the SVM classifier in two different methodologies, hierarchical and flat multi-classification schemes, to differentiate between the different AD stages for early detection purposes. The ADNI rs-fMRI dataset, employed in this study, consists of 167, 102, 129, and 114 normal, early, late mild cognitive impairment (MCI), and AD subjects, respectively. The proposed schemes achieved an average accuracy of 98.2% and 95.5% for hierarchical and flat multi-classification tasks, respectively, calculated using ten folds cross-validation. Therefore, CorrTF is considered a promising biomarker for AD early-stage identification. Moreover, the significant changes in the strengths of CorrTF connections among the different AD stages can help us identify and explore the affected brain regions and their latent associations during the progression of AD.

Unsuspected Involvement of Spinal Cord in Alzheimer Disease

Objective: Brain atrophy is an established biomarker for dementia, yet spinal cord involvement has not been investigated to date. As the spinal cord is relaying sensorimotor control signals from the cortex to the peripheral nervous system and vice-versa, it is indeed a very interesting question to assess whether it is affected by atrophy due to a disease that is known for its involvement of cognitive domains first and foremost, with motor symptoms being clinically assessed too. We, therefore, hypothesize that in Alzheimer's disease (AD), severe atrophy can affect the spinal cord too and that spinal cord atrophy is indeed an important in vivo imaging biomarker contributing to understanding neurodegeneration associated with dementia. Methods: 3DT1 images of 31 AD and 35 healthy control (HC) subjects were processed to calculate volume of brain structures and cross-sectional area (CSA) and volume (CSV) of the cervical cord [per vertebra as well as the C2-C3 pair (CSA23 and CSV23)]. Correlated features (ρ > 0.7) were removed, and the best subset identified for patients' classification with the Random Forest algorithm. General linear model regression was used to find significant differences between groups (p ≤ 0.05). Linear regression was implemented to assess the explained variance of the Mini-Mental State Examination (MMSE) score as a dependent variable with the best features as predictors. Results: Spinal cord features were significantly reduced in AD, independently of brain volumes. Patients classification reached 76% accuracy when including CSA23 together with volumes of hippocampi, left amygdala, white and gray matter, with 74% sensitivity and 78% specificity. CSA23 alone explained 13% of MMSE variance. Discussion: Our findings reveal that C2-C3 spinal cord atrophy contributes to discriminate AD from HC, together with more established features. The results show that CSA23, calculated from the same 3DT1 scan as all other brain volumes (including right and left hippocampi), has a considerable weight in classification tasks warranting further investigations. Together with recent studies revealing that AD atrophy is spread beyond the temporal lobes, our result adds the spinal cord to a number of unsuspected regions involved in the disease. Interestingly, spinal cord atrophy explains also cognitive scores, which could significantly impact how we model sensorimotor control in degenerative diseases with a primary cognitive domain involvement. Prospective studies should be purposely designed to understand the mechanisms of atrophy and the role of the spinal cord in AD.

Prognostic Value of Serum Copper for Post-Stroke Clinical Recovery: A Pilot Study

The clinical course after ischemic stroke can vary considerably despite similar lesions and clinical status at the onset of symptoms, suggesting that individual factors modulate clinical recovery. Here, we sought to test the working hypothesis that elevated copper values provide prognostic information, and specifically predict worse clinical recovery. We further sought to support previous findings regarding metal metabolism in acute stroke. We assessed total antioxidant status, oxidative stress factors (peroxides) and metal metabolism markers (iron, copper, ceruloplasmin concentration and activity, ferritin, and transferrin) in the acute phase (2–10 days from symptom onset) in 30 patients affected by unilateral middle cerebral artery (MCA) stroke. A longitudinal assessment of clinical deficit was performed in the acute and stabilized phases (typically 6 months post-stroke) using the National Institutes of Health Stroke Scale (NIHSS). In identifying recovery-related factors, we considered effective recovery (ER), calculated as the ratio between actual NIHSS recovery and the total potential recovery. This allows an estimation of the actual recovery adjusted for the patient’s initial condition. In the acute phase, clinical severity was correlated with increased peroxide concentrations, and lower iron levels. Less successful clinical recovery was correlated with increased acute copper levels, which entered a multiple regression model that explained 24% of ER variance. These pilot data suggest that, in the acute phase of an ischemic stroke, copper may provide useful information about clinical recovery.

Predicting individual brain functional connectivity using a Bayesian hierarchical model

Network-oriented analysis of functional magnetic resonance imaging (fMRI), especially resting-state fMRI, has revealed important association between abnormal connectivity and brain disorders such as schizophrenia, major depression and Alzheimer's disease. Imaging-based brain connectivity measures have become a useful tool for investigating the pathophysiology, progression and treatment response of psychiatric disorders and neurodegenerative diseases. Recent studies have started to explore the possibility of using functional neuroimaging to help predict disease progression and guide treatment selection for individual patients. These studies provide the impetus to develop statistical methodology that would help provide predictive information on disease progression-related or treatment-related changes in neural connectivity. To this end, we propose a prediction method based on Bayesian hierarchical model that uses individual's baseline fMRI scans, coupled with relevant subject characteristics, to predict the individual's future functional connectivity. A key advantage of the proposed method is that it can improve the accuracy of individualized prediction of connectivity by combining information from both group-level connectivity patterns that are common to subjects with similar characteristics as well as individual-level connectivity features that are particular to the specific subject. Furthermore, our method also offers statistical inference tools such as predictive intervals that help quantify the uncertainty or variability of the predicted outcomes. The proposed prediction method could be a useful approach to predict the changes in individual patient's brain connectivity with the progression of a disease. It can also be used to predict a patient's post-treatment brain connectivity after a specified treatment regimen. Another utility of the proposed method is that it can be applied to test-retest imaging data to develop a more reliable estimator for individual functional connectivity. We show there exists a nice connection between our proposed estimator and a recently developed shrinkage estimator of connectivity measures in the neuroimaging community. We develop an expectation-maximization (EM) algorithm for estimation of the proposed Bayesian hierarchical model. Simulations studies are performed to evaluate the accuracy of our proposed prediction methods. We illustrate the application of the methods with two data examples: the longitudinal resting-state fMRI from ADNI2 study and the test-retest fMRI data from Kirby21 study. In both the simulation studies and the fMRI data applications, we demonstrate that the proposed methods provide more accurate prediction and more reliable estimation of individual functional connectivity as compared with alternative methods.

A combination of physical activity and computerized brain training improves verbal memory and increases cerebral glucose metabolism in the elderly

Physical exercise interventions and cognitive training programs have individually been reported to improve cognition in the healthy elderly population; however, the clinical significance of using a combined approach is currently lacking. This study evaluated whether physical activity (PA), computerized cognitive training and/or a combination of both could improve cognition. In this nonrandomized study, 224 healthy community-dwelling older adults (60-85 years) were assigned to 16 weeks home-based PA (n=64), computerized cognitive stimulation (n=62), a combination of both (combined, n=51) or a control group (n=47). Cognition was assessed using the Rey Auditory Verbal Learning Test, Controlled Oral Word Association Test and the CogState computerized battery at baseline, 8 and 16 weeks post intervention. Physical fitness assessments were performed at all time points. A subset (total n=45) of participants underwent [(18)F] fluorodeoxyglucose positron emission tomography scans at 16 weeks (post-intervention). One hundred and ninety-one participants completed the study and the data of 172 participants were included in the final analysis. Compared with the control group, the combined group showed improved verbal episodic memory and significantly higher brain glucose metabolism in the left sensorimotor cortex after controlling for age, sex, premorbid IQ, apolipoprotein E (APOE) status and history of head injury. The higher cerebral glucose metabolism in this brain region was positively associated with improved verbal memory seen in the combined group only. Our study provides evidence that a specific combination of physical and mental exercises for 16 weeks can improve cognition and increase cerebral glucose metabolism in cognitively intact healthy older adults.