Alzheimers Disease Diagnosis using Machine Learning: A Review

Alzheimers Disease AD is an acute neuro disease that degenerates the brain cells and thus leads to memory loss progressively. It is a fatal brain disease that mostly affects the elderly. It steers the decline of cognitive and biological functions of the brain and shrinks the brain successively, which in turn is known as Atrophy. For an accurate diagnosis of Alzheimers disease, cutting edge methods like machine learning are essential. Recently, machine learning has gained a lot of attention and popularity in the medical industry. As the illness progresses, those with Alzheimers have a far more difficult time doing even the most basic tasks, and in the worst case, their brain completely stops functioning. A persons likelihood of having early-stage Alzheimers disease may be determined using the ML method. In this analysis, papers on Alzheimers disease diagnosis based on deep learning techniques and reinforcement learning between 2008 and 2023 found in google scholar were studied. Sixty relevant papers obtained after the search was considered for this study. These papers were analysed based on the biomarkers of AD and the machine-learning techniques used. The analysis shows that deep learning methods have an immense ability to extract features and classify AD with good accuracy. The DRL methods have not been used much in the field of image processing. The comparison results of deep learning and reinforcement learning illustrate that the scope of Deep Reinforcement Learning DRL in dementia detection needs to be explored.Comment: 10 pages and 3 figure

Developing novel non-invasive MRI techniques to assess cerebrospinal fluid-interstitial fluid (CSF-ISF) exchange

The pathological cascade of events in Alzheimer’s disease (AD) is initiated decades prior to the onset of symptoms. Despite intensive research, the relative time-course/interaction of these events is yet to be determined. Recent evidence suggests that impairments to brain clearance (facilitated by the compartmental exchange of cerebrospinal-fluid (CSF) with interstitial-fluid (ISF)), contributes to the build-up of amyloid and tau (AD hallmarks). Therefore, abnormalities in CSF-ISF exchange dynamics, may represent an early driver of downstream events. Clinical evaluation of this hypothesis is hampered due to the lack of non-invasive CSF-ISF exchange assessment techniques. In this thesis, the primary aim was to develop a physiologically relevant, non-invasive CSF-ISF exchange assessment technique that would circumvent the limitations associated with current procedures (primarily their invasiveness). Towards this goal, animal studies were conducted to investigate the feasibility of a contrast enhanced-magnetic resonance imaging (CE-MRI) approach as a potential non-invasive CSF-ISF exchange imaging technique. Another aim of this thesis was to investigate whether the proposed MRI platform could detect abnormalities in CSF-ISF exchange, a condition hypothesised to occur in the early stages of AD. As such, pharmacological intervention studies were conducted to alter CSF-ISF exchange dynamics. CE-MRI, in conjunction with high-level image post-processing, demonstrated high sensitivity to physiological CSF-ISF exchange. This novel, non-invasive platform, captured dynamic, whole-brain infiltration of contrast agent from the blood to the CSF and into the parenchyma, via a pathway named ‘VEntricular-Cerebral TranspORt (VECTOR)’. Additionally, the platform detected significant abnormalities in CSF-ISF exchange following pharmacological intervention, demonstrating the potential of VECTOR in the study of the parenchymal accumulation of aberrant proteins. Development of this platform is a breakthrough step towards the clinical assessment of CSF-ISF exchange abnormalities to study its role in disease onset/progression, an approach that may inform understanding of the causal sequence of pathological events that occurs in AD development

Modeling and simulation applications with potential impact in drug development and patient care

Indiana University-Purdue University Indianapolis (IUPUI)Model-based drug development has become an essential element to potentially make drug development more productive by assessing the data using mathematical and statistical approaches to construct and utilize models to increase the understanding of the drug and disease. The modeling and simulation approach not only quantifies the exposure-response relationship, and the level of variability, but also identifies the potential contributors to the variability. I hypothesized that the modeling and simulation approach can: 1) leverage our understanding of pharmacokinetic-pharmacodynamic (PK-PD) relationship from pre-clinical system to human; 2) quantitatively capture the drug impact on patients; 3) evaluate clinical trial designs; and 4) identify potential contributors to drug toxicity and efficacy. The major findings for these studies included: 1) a translational PK modeling approach that predicted clozapine and norclozapine central nervous system exposures in humans relating these exposures to receptor binding kinetics at multiple receptors; 2) a population pharmacokinetic analysis of a study of sertraline in depressed elderly patients with Alzheimer’s disease that identified site specific differences in drug exposure contributing to the overall variability in sertraline exposure; 3) the utility of a longitudinal tumor dynamic model developed by the Food and Drug Administration for predicting survival in non-small cell lung cancer patients, including an exploration of the limitations of this approach; 4) a Monte Carlo clinical trial simulation approach that was used to evaluate a pre-defined oncology trial with a sparse drug concentration sampling schedule with the aim to quantify how well individual drug exposures, random variability, and the food effects of abiraterone and nilotinib were determined under these conditions; 5) a time to event analysis that facilitated the identification of candidate genes including polymorphisms associated with vincristine-induced neuropathy from several association analyses in childhood acute lymphoblastic leukemia (ALL) patients; and 6) a LASSO penalized regression model that predicted vincristine-induced neuropathy and relapse in ALL patients and provided the basis for a risk assessment of the population. Overall, results from this dissertation provide an improved understanding of treatment effect in patients with an assessment of PK/PD combined and with a risk evaluation of drug toxicity and efficacy

Studying brain connectivity: a new multimodal approach for structure and function integration \u200b

Il cervello \ue8 un sistema che integra organizzazioni anatomiche e funzionali. Negli ultimi dieci anni, la comunit\ue0 neuroscientifica si \ue8 posta la domanda sulla relazione struttura-funzione. Essa pu\uf2 essere esplorata attraverso lo studio della connettivit\ue0. Nello specifico, la connettivit\ue0 strutturale pu\uf2 essere definita dal segnale di risonanza magnetica pesato in diffusione seguito dalla computazione della trattografia; mentre la correlazione funzionale del cervello pu\uf2 essere calcolata a partire da diversi segnali, come la risonanza magnetica funzionale o l\u2019elettro-/magneto-encefalografia, che consente la cattura del segnale di attivazione cerebrale a una risoluzione temporale pi\uf9 elevata. Recentemente, la relazione struttura-funzione \ue8 stata esplorata utilizzando strumenti di elaborazione del segnale sui grafi, che estendono e generalizzano le operazioni di elaborazione del segnale ai grafi. In specifico, alcuni studi utilizzano la trasformata di Fourier applicata alla connettivit\ue0 strutturale per misurare la decomposizione del segnale funzionale in porzioni che si allineano (\u201caligned\u201d) e non si allineano (\u201cliberal\u201d) con la sottostante rete di materia bianca. Il relativo allineamento funzionale con l\u2019anatomia \ue8 stato associato alla flessibilit\ue0 cognitiva, sottolineando forti allineamenti di attivit\ue0 corticali, e suggerendo che i sistemi sottocorticali contengono pi\uf9 segnali liberi rispetto alla corteccia. Queste relazioni multimodali non sono, per\uf2, ancora chiare per segnali con elevata risoluzione temporale, oltre ad essere ristretti a specifiche zone cerebrali. Oltretutto, al giorno d'oggi la ricostruzione della trattografia \ue8 ancora un argomento impegnativo, soprattutto se utilizzata per l'estrazione della connettivit\ue0 strutturale. Nel corso dell'ultimo decennio si \ue8 vista una proliferazione di nuovi modelli per ricostruire la trattografia, ma il loro conseguente effetto sullo strumento di connettivit\ue0 non \ue8 ancora chiaro. In questa tesi, ho districato i dubbi sulla variabilit\ue0 dei trattogrammi derivati da diversi metodi di trattografia, confrontandoli con un paradigma di test-retest, che consente di definire la specificit\ue0 e la sensibilit\ue0 di ciascun modello. Ho cercato di trovare un compromesso tra queste, per definire un miglior metodo trattografico. Inoltre, ho affrontato il problema dei grafi pesati confrontando alcune possibili stime, evidenziando la sufficienza della connettivit\ue0 binaria e la potenza delle propriet\ue0 microstrutturali di nuova generazione nelle applicazioni cliniche. Qui, ho sviluppato un modello di proiezione che consente l'uso dei filtri aligned e liberal per i segnali di encefalografia. Il modello estende i vincoli strutturali per considerare le connessioni indirette, che recentemente si sono dimostrate utili nella relazione struttura-funzione. I risultati preliminari del nuovo modello indicano un\u2019implicazione dinamica di momenti pi\uf9 aligned e momenti pi\uf9 liberal, evidenziando le fluttuazioni presenti nello stato di riposo. Inoltre, viene presentata una relazione specifica di periodi pi\uf9 allineati e liberali per il paradigma motorio. Questo modello apre la prospettiva alla definizione di nuovi biomarcatori. Considerando che l\u2019encefalografia \ue8 spesso usata nelle applicazioni cliniche, questa integrazione multimodale applicata su dati di Parkinson o di ictus potrebbe combinare le informazioni dei cambiamenti strutturali e funzionali nelle connessioni cerebrali, che al momento sono state dimostrate individualmente.The brain is a complex system of which anatomical and functional organization is both segregated and integrated. A longstanding question for the neuroscience community has been to elucidate the mutual influences between structure and function. To that aim, first, structural and functional connectivity need to be explored individually. Structural connectivity can be measured by the Diffusion Magnetic Resonance signal followed by successive computational steps up to virtual tractography. Functional connectivity can be established by correlation between the brain activity time courses measured by different modalities, such as functional Magnetic Resonance Imaging or Electro/Magneto Encephalography. Recently, the Graph Signal Processing (GSP) framework has provided a new way to jointly analyse structure and function. In particular, this framework extends and generalizes many classical signal-processing operations to graphs (e.g., spectral analysis, filtering, and so on). The graph here is built by the structural connectome; i.e., the anatomical backbone of the brain where nodes represent brain regions and edge weights strength of structural connectivity. The functional signals are considered as time-dependent graph signals; i.e., measures associated to the nodes of the graph. The concept of the Graph Fourier Transform then allows decomposing regional functional signals into, on one side, a portion that strongly aligned with the underlying structural network (\u201caligned"), and, on the other side, a portion that is not well aligned with structure (\u201cliberal"). The proportion of aligned-vs-liberal energy in functional signals has been associated with cognitive flexibility. However, the interpretation of these multimodal relationships is still limited and unexplored for higher temporal resolution functional signals such as M/EEG. Moreover, the construction of the structural connectome itself using tractography is still a challenging topic, for which, in the last decade, many new advanced models were proposed, but their impact on the connectome remains unclear. In the first part of this thesis, I disentangled the variability of tractograms derived from different tractography methods, comparing them with a test-retest paradigm, which allows to define specificity and sensitivity of each model. I want to find the best trade-off between specificity and sensitivity to define the best model that can be deployed for analysis of functional signals. Moreover, I addressed the issue of weighing the graph comparing few estimates, highlighting the sufficiency of binary connectivity, and the power of the latest-generation microstructural properties in clinical applications. In the second part, I developed a GSP method that allows applying the aligned and liberal filters to M/EEG signals. The model extends the structural constraints to consider indirect connections, which recently demonstrated to be powerful in the structure/function link. I then show that it is possible to identify dynamic changes in aligned-vs-liberal energy, highlighting fluctuations present motor task and resting state. This model opens the perspective of novel biomarkers. Indeed, M/EEG are often used in clinical applications; e.g., multimodal integration in data from Parkinson\u2019s disease or stroke could combine changes of both structural and functional connectivity

Identifying regulators of synaptic stability during normal healthy ageing

The loss and dysfunction of selected populations of synapses is characteristic of mammalian brain ageing and alterations in these receptive compartments are considered to underpin age-related cognitive decline. Discrete neuro-anatomical regions of the cortical architecture harbour disparate populations of synapses that demonstrate significant heterogeneity with regards to advancing age. Of particular interest is the hippocampus, which is selectively vulnerable during ageing. The hippocampal synaptic architecture exhibits subtle structural and biophysical alterations, which are considered to promote the manifestation of cognitive symptoms in aged patients. This notion of “selective synaptic vulnerability” has been the focal point of a multitude of morphological studies investigating age-related cognitive decline, which have often provided tentative conclusions as to how this phenomenon may be regulated. The molecular correlates bolstering the reported age-dependent morphological and functional shift remain elusive and studies are only now beginning to unravel how discrete organelles, proteins and signalling cascades may hierarchically or synergistically attenuate synaptic function. Until there is considerable comprehension of how functional mediators drive the biochemical substrates regulating age-related cognitive decline, there are limited strategic avenues for the development of efficacious therapeutic interventions that promote successful ageing. To address the phenomenon of selective synaptic vulnerability, we have utilised an unbiased combinatorial approach, including quantitative proteomic analyses coupled with in vivo candidate assessments in lower order animals (Drosophila), to temporally profile regional synapse and synaptic mitochondrial biochemistry during normal healthy ageing. We begin by demonstrating that cortical mitochondria located at the synaptic terminal are morphologically distinct from non-synaptic mitochondria in adult rodents and human patients. Biochemical isolation and purification of discrete mitochondrial subpopulations from control adult rat fore-brain enabled generation of synaptic and non-synaptic mitochondrial molecular fingerprints using quantitative proteomics, which revealed that expression of the mitochondrial proteome is highly dependent on subcellular localisation. We subsequently demonstrate that the molecular differences observed between mitochondrial sub-populations are capable of selectively influencing synaptic morphology in-vivo. Next, we sought to examine how the synaptic mitochondrial proteome was dynamically and temporally regulated throughout ageing to determine whether protein expression changes within the mitochondrial milieu are actively regulating the age-dependent vulnerability of the synaptic compartment. Proteomic profiling of wild-type mouse cortical synaptic and non-synaptic mitochondria across the lifespan revealed significant age-dependent heterogeneity between mitochondrial subpopulations, with aged organelles exhibiting unique protein expression profiles. Recapitulation of aged synaptic mitochondrial protein expression at the Drosophila neuromuscular junction has the propensity to perturb the synaptic architecture, demonstrating that temporal regulation of the mitochondrial proteome may directly modulate the stability of the synapse in vivo. Although we had comprehensively characterised the temporal regulation of rodent cortical mitochondrial subpopulations, providing a number of novel candidates that may be mediating synaptic vulnerability during ageing, we sought to establish whether similar alterations were occurring in the primate brain. Using synaptic isolates from neuroanatomically distinct age-resistant (occipital cortex) and age-vulnerable (hippocampus) regions, we demonstrate that synaptic ageing is brainregion dependent and that discrete populations of synapses significantly differ at a biochemical level in the healthy human and non-human primate brain. Recapitulation of aged hippocampal protein expression with genetic manipulation in vivo revealed numerous novel candidates that have the propensity to significantly modulate multiple morphological parameters at the synapse. Furthermore, we demonstrate that several of these candidates sit downstream of TGFβ1 and activation of the TGFβ1 signalling cascade in hippocampal synaptic populations drives the aberrant expression of selected candidates during ageing. Finally, we show that selective pharmacological inhibition of this pathway rescues synaptic phenotypes in multiple candidate lines. The data affirmed that activation of the TGFβ1 transduction pathway modulates synaptic stability and thus may contribute to the selective vulnerability of hippocampal synapses during ageing

Orally Available Near Infrared Imaging Agents for the Early Detection of Diseases

Early detection and treatment of diseases has the potential to dramatically improve patient outcomes. Diseases like cancer have shown remarkably higher survival rates when the cancer is detected early, before it has had a chance to metastasize and migrate to different regions. One way to increase rates of early detection is to implement annual screenings. Current screening methods often focus on blood tests, which gather molecular information from the circulation, or imaging, which provides anatomical details. Molecular imaging has the ability to provide both types of information, but the high cost and radiation risk often preclude its use in population screening. In this thesis, we hypothesized that near-infrared fluorescent imaging agents could be administered orally and yield sufficient contrast for disease diagnosis. The use of NIR fluorescent targeting ligands provides both spatial and molecular information while making the entire process fast, inexpensive, completely non-invasive, and safe with the use of non-ionizing radiation. For proof-of-concept studies to develop this novel technique, we selected integrin of the form αvβ3 as the target, and a high affinity peptidomimetic as the ligand. The major challenge of developing an orally available imaging agent is that orally available drugs are typically small in size and lipophilic in nature, while imaging agents tend to be larger in size and hydrophilic. In spite of these challenges, an IRDye800CW-labeled agent had an oral absorption of 2.3% and was selected for studies in the detection of two diseases: breast cancer and rheumatoid arthritis. Mammography uses x-rays to detect suspicious lesions when screening for breast cancer but only provides anatomical data, which has lead to high false positive rates and an estimated $4 billion in expenditure due to overdiagnosis. The IRDye800CW agent was dosed at 5 mg/kg in an orthotopic tumor xenograft mouse model. Live animal imaging at 6, 24 and 48 hours post administration showed the highest target to background ratio of ~4 at 48 hours and histology showed high uptake of the agent by macrophages and breast cancer cells. Rheumatoid arthritis (RA) is an autoimmune disease that leads to largely irreversible joint damage over time, but effective treatments are available. Therefore, there is intense interest in early detection of RA to prevent further damage, and some studies have even indicated that the disease could be cured if detected early. However, current methods lack the sensitivity to detect RA at an early stage. Oral delivery of the IRDye800CW agent in a collagen antibody induced arthritis mouse model showed significantly higher uptake in the inflamed joints compared to healthy joints. To scale the expected signal to clinically relevant depths, we developed a 3D COMSOL model for optical simulations of RA detection in the human hand. The simulations showed that for target to background concentration ratios of the imaging agent of 5.5 and 6.5, there was 95% and 98% probability of detection of the inflamed joint. The in vivo mouse model had an estimated target to background concentration ratio of ~20, which makes the detection of RA in humans very promising. This dissertation demonstrates the oral delivery of molecular imaging agents for the detection of breast cancer and RA in relevant mouse models. These studies provide the foundation to develop a range of oral molecular imaging agents for other biomarkers and diseases with the potential for earlier diagnosis to improve patient outcomes.PHDChemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147662/1/sumitbh_1.pd

MUSCARINIC MODULATION OF BASOLATERAL AMYGDALA

The basolateral amygdala (BL) receives a dense cholinergic innervation from the basal forebrain. Despite the importance of muscarinic acetylcholine receptors (mAChRs) in fear learning, consolidation, and extinction, there have been no studies that have systematically investigated the functional role of mAChRs in regulating emotional processing in the BL. To address this critical knowledge gap we combined brain slice whole-cell recording, optogenetics, and immunohistochemistry to determine how muscarine, acting on mAChRs, regulates neuronal oscillations, synaptic transmission and plasticity in the BL. Neurons in the BL oscillate rhythmically during emotional processing, which are thought to be important to integrate sensory inputs, allow binding of information from different brain areas and facilitate synaptic plasticity in target downstream structures. We found that muscarine induced theta frequency rhythmic inhibitory postsynaptic potentials (IPSPs) in BL pyramidal neuron (PN). These IPSPs synchronized PN firing at theta frequencies. Recordings from neurochemically-identified interneurons revealed that muscarine selectively depolarized parvalbumin (PV)-containing, fast firing, but not PV, regular firing or somatostatin (SOM)-containing interneurons. This depolarization was mediated by M3 mAChRs. Dual cell recordings from connected interneuron-PN pair indicated that action potentials in fast firing, but not regular firing interneurons were strongly correlated with large IPSCs in BL PNs. Furthermore, selective blockade of M3, but not M1 mAChRs suppressed the rhythmic IPSCs in BL PNs. These findings suggest that muscarine induces rhythmic IPSCs in PNs by selectively depolarizing PV, fast firing interneurons through M3 mAChRs. Furthermore, we found that rhythmic IPSCs were highly synchronized between PNs throughout the BL. The BL receives extensive glutamatergic inputs from multiple brain regions and recurrent collaterals as well. They are important for fear learning and extinction, which are tightly regulated by local GABAergic inhibition. We found that mAChRs activation suppressed external glutamatergic inputs in a frequency dependent and pathway specific manner but kept recurrent glutamatergic transmission intact. In addition, muscarine disinhibited BL PNs by attenuating feedforward and GABAergic inhibition. In agreement with these observations, long term potentiation (LTP) induction was facilitated in the BL by mAChRs activation. Taken together, we provided mechanisms for cholinergic induction of thetaoscillations and facilitation of LTP in the BL

Chaperones and chaperone-substrate complexes: dynamic playgrounds for NMR spectroscopists

The majority of proteins depend on a well-defined three-dimensional structure to obtain their functionality. In the cellular environment, the process of protein folding is guided by molecular chaperones to avoid misfolding, aggregation, and the generation of toxic species. To this end, living cells contain complex networks of molecular chaperones, which interact with substrate polypeptides by a multitude of different functionalities: transport them towards a target location, help them fold, unfold misfolded species, resolve aggregates, or deliver them towards a proteolysis machinery. Despite the availability of high-resolution crystal structures of many important chaperones in their substrate-free apo forms, structural information about how substrates are bound by chaperones and how they are protected from misfolding and aggregation is very sparse. This lack of information arises from the highly dynamic nature of chaperone-substrate complexes, which so far has largely hindered their crystallization. This highly dynamic nature makes chaperone-substrate complexes good targets for NMR spectroscopy. Here, we review the results achieved by NMR spectroscopy to understand chaperone function in general and details of chaperone-substrate interactions in particular. We assess the information content and applicability of different NMR techniques for the characterization of chaperones and chaperone-substrate complexes. Finally, we highlight three recent studies, which have provided structural descriptions of chaperone-substrate complexes at atomic resolution