Development of Gaussian Learning Algorithms for Early Detection of Alzheimer\u27s Disease

Alzheimer’s disease (AD) is the most common form of dementia affecting 10% of the population over the age of 65 and the growing costs in managing AD are estimated to be $259 billion, according to data reported in the 2017 by the Alzheimer\u27s Association. Moreover, with cognitive decline, daily life of the affected persons and their families are severely impacted. Taking advantage of the diagnosis of AD and its prodromal stage of mild cognitive impairment (MCI), an early treatment may help patients preserve the quality of life and slow the progression of the disease, even though the underlying disease cannot be reversed or stopped. This research aims to develop Gaussian learning algorithms, natural language processing (NLP) techniques, and mathematical models to effectively delineate the MCI participants from the cognitively normal (CN) group, and identify the most significant brain regions and patterns of changes associated with the progression of AD. The focus will be placed on the earliest manifestations of the disease (early MCI or EMCI) to plan for effective curative/therapeutic interventions and protocols. Multiple modalities of biomarkers have been found to be significantly sensitive in assessing the progression of AD. In this work, several novel multimodal classification frameworks based on proposed Gaussian Learning algorithms are created and applied to neuroimaging data. Classification based on the combination of structural magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers is seen as the most reliable approach for high-accuracy classification. Additionally, changes in linguistic complexity may provide complementary information for the diagnosis and prognosis of AD. For this research endeavor, an NLP-oriented neuropsychological assessment is developed to automatically analyze the distinguishing characteristics of text data in MCI group versus those in CN group. Early findings suggest significant linguistic differences between CN and MCI subjects in terms of word usage, vocabulary, recall, fragmented sentences. In summary, the results obtained indicate a high potential of the neuroimaging-based classification and NLP-oriented assessment to be utilized as a practically computer aided diagnosis system for classification and prediction of AD and its prodromal stages. Future work will ultimately focus on early signs of AD that could help in the planning of curative and therapeutic intervention to slow the progression of the disease

Reward circuitry is perturbed in the absence of the serotonin transporter

The serotonin transporter (SERT) modulates the entire serotonergic system in the brain and influences both the dopaminergic and norepinephrinergic systems. These three systems are intimately involved in normal physiological functioning of the brain and implicated in numerous pathological conditions. Here we use high-resolution magnetic resonance imaging (MRI) and spectroscopy to elucidate the effects of disruption of the serotonin transporter in an animal model system: the SERT knock-out mouse. Employing manganese-enhanced MRI, we injected Mn^(2+) into the prefrontal cortex and obtained 3D MR images at specific time points in cohorts of SERT and normal mice. Statistical analysis of co-registered datasets demonstrated that active circuitry originating in the prefrontal cortex in the SERT knock-out is dramatically altered, with a bias towards more posterior areas (substantia nigra, ventral tegmental area, and Raphé nuclei) directly involved in the reward circuit. Injection site and tracing were confirmed with traditional track tracers by optical microscopy. In contrast, metabolite levels were essentially normal in the SERT knock-out by in vivo magnetic resonance spectroscopy and little or no anatomical differences between SERT knock-out and normal mice were detected by MRI. These findings point to modulation of the limbic cortical–ventral striatopallidal by disruption of SERT function. Thus, molecular disruptions of SERT that produce behavioral changes also alter the functional anatomy of the reward circuitry in which all the monoamine systems are involved

Assessment Of The Interplay Between Regional β-Amyloid Burden And White Matter Hyperintensities On Cognition And Default Mode Network In Clinically Normal Older Participants

Objective: Alzheimer’s disease (AD) and subcortical vascular dementia are considered the most common pathologic contributors to dementia in the aging population. Both frequently coexist in over 80% of community dwelling adults with dementia. The neuropathological development of AD arguably begins with β-amyloid (Aβ) deposition in the brain. This series of studies aims to test the hypothesis that early focal regional amyloid deposition in the brain is associated with cognitive performance in specific cognitive domain scores in preclinical AD (pAD) (study1). Since mixed dementia is widely recognized as the norm rather than the exception, the second study aimed to explore the relation between regional and global Aβ and WMH with core cognitive function (executive function (EF) and memory) scores in cognitively normal (CN) older adults (study2). Finally, the relationship between WMH and Aβ is strongly determined by the spatial distribution of the two pathologies, so the third study aimed to quantify Aβ in Default mode network (DMN) regions to examine whether cerebral small vessels disease (SVD) disruption of connectivity affects Aβ deposition in disconnected DMN regions (study3). Method: Global and regional Standard Uptake Value ratios (SUVr) from Aβ-PET, WMH volumes from MRI FLAIR images, and cognitive test scores were analyzed across a sample of CN participants. Linear regression models adjusted for age, sex and education used to assess the relationships between regional SUVr and cognitive test scores across 99 CN from Sanders Brown Center on Aging (study1). Moderation, and mediation modeling were used to define the interplay between global, regional Aβ and WMHs measures in relation to EF and memory composite scores outcomes at baseline and after approximately 2 years across a sample of 714 CN from the Alzheimer’s Disease Neuroimaging Initiative ADNI (study2). The association of WMH volume in anatomically defined white matter tracts of atlas-based fiber tract with Aβ SUVr specifically in connected cortical regions within DMN was tested across sample of 74 CN from ADNI3. Results: EF performance was associated with increased regional SUVr in the precuneus and posterior cingulate regions only (p \u3c 0.05). The moderation regression analysis showed additive effects of Aβ and WMH over baseline memory and EF scores (p =0.401 and 0.061 respectively) and synergistic effects over follow-up EF (p \u3c 0.05). Through mediation analysis, the data from study 2 showed that WMH affects, mediated by global and regional amyloid burden, are responsible for baseline cognitive performance deficits in memory and EF. Finally, the regression analysis from study 3 demonstrated that increased WMH volumes in superior longitudinal fasciculus (SLF) was associated with increased regional SUVr in inferior parietal lobule (IPL) (p \u3c 0.05). Conclusion: While the prevailing view in the field suggests that memory performance is the earliest clinical hallmark of AD, the present data demonstrate that changes in EF, mediated by Aβ deposition in the precuneus and posterior cingulate may precede memory decline in pAD. After adding the second key driver of cognitive decline in CN, the finding suggested that WMH dependent changes in baseline cognitive performance are related to direct effect of WMH and an indirect effect through both global and regional Aβ burden. Further studies are needed to show the longitudinal influences of WMH on Aβ distributions in participants with mixed dementia

The thalamus as a putative biomarker in neurodegenerative disorders

Objective: This review provides a brief account of the clinically relevant functional neuroanatomy of the thalamus, before considering the utility of various modalities utilised to image the thalamus and technical challenges therein, and going on to provide an overview of studies utilising structural imaging techniques to map thalamic morphology in the spectrum of neurodegenerative disorders. Methods: A systematic search was conducted for peer-reviewed studies involving structural neuroimaging modalities investigating the morphology (shape and/ or size) of the thalamus in the spectrum of neurodegenerative disorders. Results: Whilst the precise role of the thalamus in the healthy brain remains unclear, there is a large body of knowledge accumulating which defines more precisely its functional connectivity within the connectome, and a burgeoning literature implicating its involvement in neurodegenerative disorders. It is proposed that correlation of clinical features with thalamic morphology (as a component of a quantifiable subcortical connectome) will provide a better understanding of neuropsychiatric dysfunction in various neurodegenerative disorders, potentially yielding clinically useful endophenotypes and disease biomarkers. Conclusions: Thalamic biomarkers in the neurodegenerative disorders have great potential to provide clinically meaningful knowledge regarding not only disease onset and progression, but may yield targets of and perhaps a way of gauging response to future disease-modifying modalities

Enhancing Alzheimer Disease Segmentation through Adaptively Regularized Weighted Kernel-Based Clustering

Image segmentation is important in image analysis because it helps to locate objects and boundaries within a picture. This study offers Adaptively Regularized Weighted Kernel-Based Clustering (ARWKC), a unique segmentation technique built exclusively for recovering brain tissue from medical pictures. The proposed approach incorporates adaptive regularization and weighted kernel-based clustering techniques to increase the accuracy and resilience of brain tissue segmentation. The picture is initially preprocessed with the ARWKC method to improve its quality and eliminate any noise or artifacts. The adaptive regularization method is then utilized to effectively deal with the visual variation of brain tissue in clinical images. This adaptive regularization contributes to more accurate and consistent segmentation outcomes. The weighted kernel-based clustering method is then used to find and group pixels with comparable properties, with a focus on brain tissue areas. This clustering approach employs a weighted kernel function that takes into account both geographical closeness and pixel intensities, allowing the algorithm to capture local picture features and improve segmentation accuracy. Extensive experiments were conducted on a collection of medical images to evaluate the efficacy of the ARWKC algorithm. The well-known k-means clustering method, often used in image segmentation applications, was utilized as a benchmark for comparison. In terms of accuracy and resilience for brain tissue segmentation, the experimental findings showed that the ARWKC method surpasses the k-means clustering approach

Ischemic axonal injury up-regulates MARK4 in cortical neurons and primes tau phosphorylation and aggregation.

Ischemic injury to white matter tracts is increasingly recognized to play a key role in age-related cognitive decline, vascular dementia, and Alzheimer's disease. Knowledge of the effects of ischemic axonal injury on cortical neurons is limited yet critical to identifying molecular pathways that link neurodegeneration and ischemia. Using a mouse model of subcortical white matter ischemic injury coupled with retrograde neuronal tracing, we employed magnetic affinity cell sorting with fluorescence-activated cell sorting to capture layer-specific cortical neurons and performed RNA-sequencing. With this approach, we identified a role for microtubule reorganization within stroke-injured neurons acting through the regulation of tau. We find that subcortical stroke-injured Layer 5 cortical neurons up-regulate the microtubule affinity-regulating kinase, Mark4, in response to axonal injury. Stroke-induced up-regulation of Mark4 is associated with selective remodeling of the apical dendrite after stroke and the phosphorylation of tau in vivo. In a cell-based tau biosensor assay, Mark4 promotes the aggregation of human tau in vitro. Increased expression of Mark4 after ischemic axonal injury in deep layer cortical neurons provides new evidence for synergism between axonal and neurodegenerative pathologies by priming of tau phosphorylation and aggregation

Multimodal brain imaging reveals structural differences in Alzheimer's disease polygenic risk carriers: A study in healthy young adults

Background Recent genome-wide association studies have identified genetic loci that jointly make a considerable contribution to risk of developing Alzheimer’s disease (AD). Because neuropathological features of AD can be present several decades before disease onset, we investigated whether effects of polygenic risk are detectable by neuroimaging in young adults. We hypothesized that higher polygenic risk scores (PRSs) for AD would be associated with reduced volume of the hippocampus and other limbic and paralimbic areas. We further hypothesized that AD PRSs would affect the microstructure of fiber tracts connecting the hippocampus with other brain areas. Methods We analyzed the association between AD PRSs and brain imaging parameters using T1-weighted structural (n = 272) and diffusion-weighted scans (n = 197). Results We found a significant association between AD PRSs and left hippocampal volume, with higher risk associated with lower left hippocampal volume (p = .001). This effect remained when the APOE gene was excluded (p = .031), suggesting that the relationship between hippocampal volume and AD is the result of multiple genetic factors and not exclusively variability in the APOE gene. The diffusion tensor imaging analysis revealed that fractional anisotropy of the right cingulum was inversely correlated with AD PRSs (p = .009). We thus show that polygenic effects of AD risk variants on brain structure can already be detected in young adults. Conclusions This finding paves the way for further investigation of the effects of AD risk variants and may become useful for efforts to combine genotypic and phenotypic data for risk prediction and to enrich future prevention trials of AD

Comparison of resting electroencephalogram coherence in patients with mild cognitive impairment and normal elderly subjects

Mild cognitive impairment (MCI) was a condition beginning before more serious deterioration, leading to Alzheimer’s dementia (AD). MCI detection was needed to determine the patient's therapeutic management. Analysis of electroencephalogram (EEG) coherence is one of the modalities for MCI detection. Therefore, this study investigated the inter and intra-hemispheric coherence over 16 EEG channels in the frequency range of 1-30 Hz. The simulation results showed that most of the electrode pair coherence in MCI patients have decreased compared to normal elderly subjects. In inter hemisphere coherence, significant differences (p<0.05) were found in the FP1-FP2 electrode pairs. Meanwhile, significant differences (p<0.05) were found in almost all pre-frontal area connectivity of the intra-hemisphere coherence pairs. The electrode pairs were FP2-F4, FP2-T4, FP1-F3, FP1-F7, FP1-C3, FP1-T3, FP1-P3, FP1-T5, FP1-O1, F3-O1, and T3-T5. The decreased coherence in MCI patients showed the disconnection of cortical connections as a result of the death of the neurons. Furthermore, the coherence value can be used as a multimodal feature in normal elderly subjects and MCI. It is hoped that current studies may be considered for early detection of Alzheimer’s in a larger population