Unveiling New Strategies Facilitating the Implementation of Artificial Intelligence in Neuroimaging for the Early Detection of Alzheimer’s Disease

Abstract

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder with a global impact. The past few decades have witnessed significant strides in comprehending the underlying pathophysiological mechanisms and developing diagnostic methodologies for AD, such as neuroimaging approaches. Neuroimaging techniques, including positron emission tomography and magnetic resonance imaging, have revolutionized the field by providing valuable insights into the structural and functional alterations in the brains of individuals with AD. These imaging modalities enable the detection of early biomarkers such as amyloid-β plaques and tau protein tangles, facilitating early and precise diagnosis. Furthermore, the emerging technologies encompassing blood-based biomarkers and neurochemical profiling exhibit promising results in the identification of specific molecular signatures for AD. The integration of machine learning algorithms and artificial intelligence has enhanced the predictive capacity of these diagnostic tools when analyzing complex datasets. In this review article, we will highlight not only some of the most used diagnostic imaging approaches in neurodegeneration research but focus much more on new tools like artificial intelligence, emphasizing their application in the realm of AD. These advancements hold immense potential for early detection and intervention, thereby paving the way for personalized therapeutic strategies and ultimately augmenting the quality of life for individuals affected by AD.

Keywords

Alzheimer’s disease artificial intelligence biomarker machine learning neuroimaging

INTRODUCTION

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder broadly afflicting the geriatric population. It manifests through a gradual decline in cognitive faculties, such as episodic memory loss, cognitive impairment, and behavioral alterations [1]. AD represents the prevailing form of dementia, which contributes to approximately 60–80% of all dementia cases globally [2].

The accumulation of amyloid-β (Aβ) plaques outside and hyperphosphorylated tau protein tangles within specific neurons in the brain are recognized pathological hallmarks of the disease. These neuropathological changes result in synaptic dysfunction, neuronal loss, and widespread brain atrophy [3, 4]. However, in its sporadic or late-onset form AD is considered a multifactorial disease with a complex pathogenesis. To this end, the role of genetic factors in AD susceptibility are being extensively investigated, with the apolipoprotein E (APOE) ɛ4 allele emerging as a prominent genetic risk factor [5]. Over the years, notable advancements in diagnostic techniques and approaches within the field of neuroscience have been observed, offering new hope in the fight against AD. These advancements have focused on early detection and accurate diagnosis of the disease.

Neuroimaging techniques will continue to make impact and have a pivotal role in the early diagnosis of AD. Imaging modalities like magnetic resonance imaging (MRI) and functional MRI (fMRI) scanning has allowed clinicians to visualize structural and functional changes in the brain at the early stage. These scans aid in identifying the patterns of atrophy, abnormal connectivity, and changes in brain metabolism associated with AD. Moreover, advanced imaging techniques such as Aβ and tau positron emission tomography (PET) scans offer direct visualization of the accumulated amyloid plaques and neurofibrillary tangles, aiding in accurate diagnosis and monitoring of the disease [5 –7].

Significant advancements in AD diagnostics have been made through the development of biomarkers. Biomarkers are measurable substances or indicators that can be detected in biological samples, such as blood, cerebrospinal fluid (CSF), or neuroimaging scans [8, 9]. These biomarkers have provided insights into the presence and progression of AD-related changes in the brain. For instance, the measurement of Aβ and tau proteins in CSF or their detection through PET scans help in identifying individuals at risk of developing AD, those already in the early stages of the disease, or response to therapy [10]. As a result, this early detection of AD allows for timely intervention, implementation of appropriate treatment plans, and access to support services for individuals and their families. It opens avenues for clinical trials and the development of potential disease-modifying therapies, which are at present limited and therefore there is an urgent need for the development of new interventions [11]. Furthermore, accurate diagnosis helps to distinguish AD from other forms of dementia, enabling tailored care and management strategies specific to the individual’s needs.

In addition, emerging therapies, including immune-therapies, have injected fresh hope into the fight against AD.

Current therapeutics for AD encompass a range of strategies aimed at managing symptoms and slowing disease progression. Cholinesterase inhibitors, including donepezil, rivastigmine, and galantamine, are commonly prescribed to enhance acetylcholine levels, providing temporary relief for cognitive symptoms. Memantine, an NMDA receptor antagonist, modulates glutamate levels, offering symptomatic relief for cognitive and behavioral manifestations. However, current research is focused on various other therapeutic pathways including Aβ clearance, tau clearance, autophagy-lysosome, neurogenesis, neuroinflammation, oxidative stress, bioenergetics, epigenetic regulation, etc. [12].

Various therapeutic modalities are currently in phase 3 clinical investigation for AD, each targeting specific aspects of the pathology. Aducanumab, a monoclonal antibody, specifically targets Aβ to reduce plaques and oligomers. AGB101, a small molecule, emphasizes synaptic plasticity and neuroprotection. However, the approval of Aducanumab for AD by the FDA has been a subject of intense scientific scrutiny and ongoing debate. The drug, developed by Biogen, elicited concerns due to inconclusive results from its Phase 3 trials, wherein one trial met the primary endpoint while the other did not. The FDA’s decision to grant accelerated approval raised methodological questions regarding the reliability and robustness of the clinical trial data, as well as scientific uncertainties concerning the biological rationale of targeting Aβ plaques. Criticism has been directed towards the dosing strategy, with the FDA approving a higher dose despite lingering uncertainties about its therapeutic efficacy. The mandated post-approval trial to validate clinical benefits emphasizes the persistent scientific uncertainties and controversies surrounding Aducanumab’s efficacy in the treatment of AD [13].

AR1001 is designed to modulate neurotransmitter receptors, addressing both symptoms and cognitive aspects. AVP-786 focuses on behavioral symptoms through neurotransmitter receptor modulation, showcasing diverse strategies in the pursuit of effective therapeutic interventions for AD [13, 14].

In phase 2 and 1 trials, disease-modifying treatments (DMTs) and symptomatic agents are being explored. DMTs, including biologics and small molecules, target key factors like Aβ, tau, inflammation, synaptic plasticity, and neuroprotection [15]. Examples include aducanumab and lecanemab, anti-amyloid monoclonal antibodies. Small molecules like montelukast and rapamycin aim to reduce inflammation and enhance proteostasis. Symptomatic agents address cognition and behavior, acting on neurotransmitter receptors.

In phase 1 trials, therapeutic approaches like ACU193 (monoclonal antibody targeting soluble Aβ oligomers), allopregnanolone (allosteric modulator of GABA-A receptors), and ALN-APP (RNAi to decrease APP and downstream Aβ-related events) are being investigated. Symptomatic agents, such as cannabidiol, mecamylamine, and psilocybin, aim to address behavioral and cognitive symptoms through modulation of neurotransmitter receptors. These trials collectively reflect the diverse and comprehensive strategies being explored for AD [16].

In conjunction with therapeutic investigations, biomarkers played a pivotal role in past clinical trials for AD, providing objective and measurable indicators of the underlying pathophysiological processes. These biomarkers offered crucial insights into disease progression, treatment efficacy, and patient stratification. Commonly studied biomarkers included CSF levels of Aβ and tau proteins, neuroimaging markers such as PET scans measuring Aβ deposition and tau tangles, and structural and fMRI to assess brain atrophy and connectivity changes. The incorporation of biomarkers in past clinical trials enhanced precision and facilitated a deeper understanding of the disease’s dynamics, contributing to the refinement of therapeutic strategies and the development of targeted interventions. This integrative approach underscored the evolving landscape of AD research, combining past therapeutic modalities with advanced biomarker assessments to pave the way for more effective and personalized treatments [16].

Advancements in diverse computational technologies, including artificial intelligence (AI) and deep learning, are offering new hope for the development of superior diagnostic approaches in medical areas related to imaging, including in the field on neurodegenerative diseases [17, 18].

This article provides a comprehensive review of the advancements in diagnostic modalities for AD, which is greatly contributing to the understanding of the disease, resulting in improved patient care and the development of novel and more effective potential therapeutic strategies. Although challenges persist in finding a cure for AD, these diagnostic advancements offer optimism for enhanced disease management and comprehensive support for individuals affected by this devastating condition.

UNLOCKING THE PATHOLOGICAL LANDSCAPE OF ALZHEIMER’S DISEASE THROUGH NEUROIMAGING TECHNIQUES

AD has emerged as a prominent public health concern, necessitating precise and timely diagnosis for optimal intervention and management strategies. Neuroimaging techniques have emerged as potent tools for investigating the underlying pathological mechanisms of AD and have contributed substantially into our understanding of the disease progression onset and progression [1, 19].

Neuroimaging techniques uphold the potential for employing these modalities in a longitudinal manner within the context of neurodegenerative disorders and other neurological conditions. The techniques applied for AD (as illustrated in Fig. 1) are broadly classified into three distinct categories, each of which provides unique insights into the underlying pathological changes associated with AD [20]: 1) Structural neuroimaging; 2) Functional neuroimaging; and 3) Molecular neuroimaging.

Fig. 1

Schematic representation of different neuroimaging modalities.

The below discussion encompasses a range of diverse neuroimaging techniques that are extensively employed in the AD field.

EXPLORING NEUROANATOMY: A JOURNEY THROUGH STRUCTURAL NEUROIMAGING

Structural neuroimaging focuses on capturing the anatomical changes and structural abnormalities in the brain associated with AD. Techniques such as MRI and computed tomography (CT) are commonly employed [21]. Structural MRI provides detailed images of brain structures, which allows measurement of regional brain volumes, cortical thickness, and the detection of patterns of brain atrophy, particularly in areas such as the hippocampus and parietal cortex, which are known to be affected in AD [22]. CT scans, on the other hand, offer information about brain structure using X-rays.

Detecting AD-related structural changes: voxel-based morphometry

Voxel-based morphometry (VBM), a structural MRI which is widely employed neuroimaging methodology enables the quantitative assessment of regional variations in brain volume and cortical thickness [23]. Studies employing VBM consistently reveal significant reductions in hippocampal volume and cortical thinning in individuals diagnosed with AD when compared to healthy matched controls. These findings correspond to the characteristic neuronal loss and atrophy observed in brain regions crucial for memory formation and retrieval [24]. The volumetric reduction of the hippocampus, serves as a prominent hallmark feature of AD pathology. Moreover, cortical thinning is evident in regions such as the entorhinal cortex, temporal lobes, and prefrontal cortex, which are critically implicated in various cognitive functions affected by AD [25]. VBM-based investigations provide valuable quantitative insights into the structural alterations associated with AD, contributing to our understanding of the disease’s neuropathology at a regional level.

In spite of the significant benefits of VBM for evaluating brain structure, this technique presents several challenges that need to be addressed. These include registration errors that can introduce inaccuracies, partial volume effects that impact voxel classification, limited spatial resolution hindering the detection of subtle changes, and the absence of temporal information for tracking disease progression. Additionally, confounding factors such as age, gender, and comorbidities can influence brain structure independently of AD, requiring careful consideration during analysis of the data obtained with VBM [26].

However, despite the challenges, VBM retains its significance as a valuable tool for investigating brain structural alterations. By addressing these limitations and synergistically combining VBM with other complementary imaging techniques and biomarkers, researchers have the potential to further advance the understanding of the underlying neurodegenerative processes in AD. This integrative approach holds promise for improving diagnostic accuracy, facilitating early detection, and enabling the development of targeted therapeutic strategies.

Deciphering microstructural changes in AD: a diffusion tensor imaging study

Diffusion tensor imaging (DTI) is a non-invasive method of neuroimaging that is typically obtained using MRI which offers valuable information about the structural health of white matter pathways within the brain. Through the analysis of both the direction and magnitude of water diffusion, DTI facilitates the generation of images and quantitative metrics that serve to characterize the organizational features of white matter fibers. Parameters such as fractional anisotropy (FA) and mean diffusivity (MD) are employed to delineate and quantify the structural characteristics of these neural pathways. This approach allows for a comprehensive assessment of white matter microarchitecture, providing valuable insights into the intricacies of neural connectivity and structural integrity in the brain.

In the context of AD, DTI has substantiated its utility as an invaluable tool for investigating white matter alterations concomitant with the pathology of the disease. This aspect is very important since AD is known to affect not only gray matter regions, but also the white matter tracts that connect them [27].

DTI studies in AD patients have consistently demonstrated specific changes in diffusion parameters. Reduced fractional anisotropy (FA) and increased mean diffusivity (MD) are commonly observed in affected individuals. FA reflects the directionality and coherence of water diffusion within white matter tracts, while MD represents the magnitude of diffusion. The reductions in FA and increases in MD observed in AD patients suggest compromised white matter integrity [28].

The observed alterations in DTI metrics are particularly prominent in key white matter tracts that connect regions affected by AD pathology. As an example, the fornix, a structure pivotal for memory function, frequently displays reduced FA and elevated MD in individuals diagnosed with AD. Similarly, the cingulum, involved in various cognitive processes, shows altered DTI metrics in AD. These changes in white matter integrity within specific tracts provide valuable insights into the structural disruption occurring in AD [29].

The DTI-derived metrics of white matter integrity have the potential to serve as biomarkers for disease progression in AD. Longitudinal studies utilizing DTI have revealed that fluctuations in FA and MD are intricately associated with the trajectory of cognitive decline. Consequently, the observed alterations in white matter integrity via DTI hold promise as a means to longitudinally monitor disease progression (like other imaging techniques) and assess the efficacy of interventions within the context of clinical trials [30]. Furthermore, DTI findings highlight the relevance of white matter alterations in AD-related cognitive impairments. The disrupted white matter integrity observed in DTI studies correlates with the severity of cognitive symptoms, including memory and executive function deficits, in AD patients [31].

EXPLORING THE INNER WORKINGS OF THE BRAIN: AN INTRODUCTION TO FUNCTIONAL NEUROIMAGING TECHNIQUES

Functional neuroimaging techniques aim to assess the brain’s functional activity and connectivity, primarily focusing on changes in blood flow and metabolic activity associated with AD [32]. The most widely used method is fMRI, which detects changes in blood oxygenation levels related to neuronal activity (known as the BOLD, i.e., blood oxygenation level-dependent signal). Moreover, functional connectivity analyses based on fMRI data can identify disruptions in functional networks, revealing alterations in brain connectivity patterns associated with AD [33, 34].

Functional MRI: Investigating neural activity and connectivity in the human brain

fMRI is a robust neuroimaging modality that enables the assessment of cerebral activity and functional connectivity, providing critical insights into the functional alterations in AD [35, 36].

It allows the measurement of brain activation either at rest or in response to cognitive tasks or stimuli that engage specific brain regions and networks. It does not require the injection of contrast agents or radiation exposure, and can be repeated multiple times during longitudinal studies [18]. It provides both high temporal and spatial resolution as compared to PET; however, temporal resolution is lower than other modalities such as electroencephalography (EEG) and magnetoencephalography.

Numerous studies have investigated brain activation patterns in individuals with AD compared to those with normal cognition. These studies have consistently found decreased brain activity in parietal and hippocampal regions, which are known to be affected by the disease. Additionally, individuals with AD have shown higher activity in primary cortices, which are less affected by the disease.

Furthermore, fMRI studies have also examined the differences in activation patterns between individuals with mild cognitive impairment (MCI), a condition often considered a prodromal stages of AD and healthy controls. These studies have also identified distinct patterns of fMRI activation responses that differentiate MCI from healthy individuals [37 –39].

Resting-state fMRI investigations have also played a pivotal role in unveiling disrupted patterns of functional connectivity, particularly within the default mode network (DMN), intricately involved in introspection and memory processing. Perturbations in DMN connectivity have emerged as potential biomarkers for early detection of AD, facilitating the identification of individuals at risk even prior to the manifestation of clinical symptoms [40]. The findings highlight the utility of fMRI in elucidating the neural underpinnings of AD and suggest the potential of DMN connectivity as a promising diagnostic marker for preclinical stages of the disease. The application of fMRI in AD clinical research contributes significantly to understanding of the functional alterations in the brain and may facilitate the development of early intervention strategies for the disease [41].

Concurrently, task-based fMRI paradigms have yielded significant findings by elucidating abnormal activation patterns within cognitive processes affected by AD, such as memory encoding and retrieval [42]. By employing specific cognitive tasks during fMRI acquisitions, task-based experiments have consistently revealed atypical patterns of brain activation in AD patients compared to matched healthy controls. Notably, AD patients commonly exhibit diminished activation in the hippocampus and other memory-related regions during memory encoding tasks [43]. These task-based fMRI findings provide anatomical and structural insights on the underlying functional deficits that contribute to cognitive impairments in AD.

PRECISION AT THE MOLECULAR LEVEL: ADVANCING NEUROSCIENTIFIC UNDERSTANDING THROUGH STATE-OF-THE-ART MOLECULAR NEUROIMAGING TECHNIQUE

Molecular neuroimaging techniques play a pivotal role in the field of neuroscience, particularly in the study and diagnosis of neurodegenerative disorders such as AD. Their significance lies in their ability to target specific molecular markers intricately linked to the pathological processes of the disease. In the case of AD, two key markers, Aβ plaques and hyperphosphorylated tau, represent the prominent features associated with the condition. To elucidate and quantify these markers, researchers commonly employ radiotracers such as Pittsburgh compound B (PiB) for detecting amyloid deposition and fluorodeoxyglucose (FDG) for assessing glucose metabolism. This approach facilitates a comprehensive understanding of the disease’s underlying pathology by enabling the precise visualization and measurement of molecular changes in the brain, providing valuable insights for both research and clinical applications [44 –46].

Multiple MRI and PET neuroimaging biomarkers for AD have undergone robust validation in multicenter studies and were integrated into the diagnostic criteria outlined by the Alzheimer’s Association and the National Institute on Aging (NIA). These biomarkers comprise:

Increased binding of [¹¹C]-Pittsburgh Compound B (¹¹C-PIB) to amyloid plaques: This biomarker indicates elevated levels of Aβ protein.

Elevated binding of ¹⁸F-florbetapir (Amyvid) on PET scans to amyloid plaques: This PET tracer specifically binds to amyloid plaques, providing additional confirmation of Aβ accumulation in the brain.

Reduced uptake of [¹⁸F]-fluoro-2-deoxy-dglucose (¹⁸FDG), indicating compromised glucose metabolism: A reduction in glucose uptake in specific brain regions is indicative of neuronal dysfunction and is associated with AD progression [47].

Amyloid-β protein

The Aβ protein is recognized for its involvement in the creation of amyloid plaques observed in the brains of AD patients. Under typical physiological conditions, the generation of the Aβ protein occurs as a result of the regular processing of the amyloid-β protein precursor (AβPP) through a sequential process involving β-secretase followed by the gamma-secretase complex. The production and clearance of Aβ are tightly regulated in normal condition, ensuring a balance in its levels within the central nervous system. Aβ protein exerts influence on synaptic plasticity, neuronal development, and antioxidant activity within the brain [48, 49]. However, in AD, there is a disturbance in the equilibrium between Aβ production and clearance, leading to the accumulation of Aβ in the brain. The abnormal buildup of Aβ triggers a series of pathological processes, including inflammation and oxidative stress. As a result, the amalgamation of these pathological processes ultimately leads to neuronal dysfunction and synaptic connection loss [50].

PET imaging with radiotracers targeting Aβ, such as PiB or florbetapir, allows for the detection and quantification of Aβ deposition in the brains of individuals with AD. Increased Aβ deposition, particularly in regions like the hippocampus and cortical areas, is associated with the presence and progression of AD pathology [51]. These imaging techniques provide valuable insights into the extent of Aβ burden in the brain and assist in the diagnosis and monitoring of AD.

Tau protein

Tau is an abundant neuronal protein expressed throughout the nervous system, crucial for the promotion and stabilization of axonal microtubules. Its function is regulated by a dynamic equilibrium between a less phosphorylated state, where it associates with axonal microtubules, and a more phosphorylated state, where it becomes insoluble within the axoplasm [52]. This delicate balance is mediated by the activity of different axonal kinases and phosphatases. Perturbations in this equilibrium induce conformational changes in tau, which lead to its aggregation and altered solubility [53]. These alterations in tau structure confer resistance to cellular clearance mechanisms, including autophagy, impeding the efficient removal of tau species. In AD, abnormal accumulation of phosphorylated tau protein leads to the formation of neurofibrillary tangles. PET imaging use tau-specific radiotracers, such as flortaucipir (FTP) or AV-1451 which allows researchers to visualize and quantify the extent of tau pathology in the brain [54]. The authors discuss the development and utilization of various tau-specific PET radiotracers, such as AV-1451 (flortaucipir), THK5351, and PBB3, which selectively bind to tau deposits which enabled the visualization and measurement of tau burden, aiding in the diagnosis and monitoring of tauopathies. Tau imaging using PET was initially demonstrated with a radioactive compound called 2-(1-[6-(dimethylamino)-2-naphthyl]ethylidene)malononitrile (DDNP) labeled with fluorine-18 (¹⁸F-DDNP) [55]. This compound showed longer retention times in individuals diagnosed with AD and MCI, when compared to healthy individuals.

Another radiotracer called ¹¹C-phenyl/pyridinyl-butadienyl-benzothiazoles/benzothiazoliums (¹¹C-PBB3) also exhibited good retention in the brains of individuals with AD and other tau-related disorders thus aiding in the diagnosis and monitoring of the disease [56].

James et al., highlighted the significance of tau PET imaging as a valuable tool for non-invasive detection and quantification of tau aggregates in the living brain [57].

Another study demonstrated the potential of two tau-specific radiotracers, [¹⁸F]-THK5317 and [¹⁸F]-S16, for in vivo imaging of tau pathology in AD [58]. The study involved a small cohort of AD patients whose PET scans using both [¹⁸F]-THK5317 and [¹⁸F]-S16 radiotracers. The participants underwent clinical and cognitive assessments, including Aβ PET imaging, to confirm the diagnosis of AD and rule out other potential causes of cognitive impairment. The results demonstrated promising findings regarding the imaging capabilities of both [¹⁸F]-THK5317 and [¹⁸F]-S16. The radiotracers exhibited high binding affinity to tau aggregates, allowing for accurate visualization of tau pathology in the brain. Quantitative analysis revealed significant correlations between tracer uptake and cognitive impairment, further supporting the potential clinical utility of these imaging techniques.

Interestingly, the authors also explored the association between tau deposition and Aβ pathology, another hallmark of AD. The researchers found that tau accumulation patterns observed with [¹⁸F]-THK5317 and [¹⁸F]-S16 were consistent with the distribution of Aβ plaques, suggesting a close relationship between these two main pathological features in AD.

Glucose metabolism

Over time, researchers have extensively employed the ATN framework to classify biomarkers associated with AD, wherein amyloid (A), neurodegeneration (N), and phosphorylated tau (T), serve as its constituent elements. Well-established markers, including Aβ plaques and tau tangles, have been identified as robust indicators of AD pathology [59, 60]. However, emerging scientific investigations have shed light on the prevailing presence of impaired glucose metabolism in the brains of individuals with AD. Various comparative studies, employing age-matched cohorts of both healthy individuals and those with AD, revealed reduced glucose utilization in AD brains [61]. As a result, evaluating metabolism of glucose utilization stands as a compelling and influential imaging biomarker with significant potential for the early detection.

Under normal physiological conditions, the human brain is responsible for approximately 20% of the body’s total energy expenditure. This high metabolic demand is attributed to the intricate neural processes and cognitive functions carried out by the brain [62]. Changes in cerebral glucose metabolic rate and glucose utilization are indicative of alterations in synaptic excitability and neuronal activity. The ¹⁸FDG-PET has emerged as one of the most extensively studied PET imaging technique and has received regulatory approval as an AD biomarker [63].

A substantial number of studies demonstrate the effectiveness and utility of various imaging techniques in the delicate process of achieving an accurate diagnosis of AD or monitoring its progression. For instance, Shea et al. in their study demonstrated of the diagnostic impact of ¹⁸FDG PET and ¹¹C-PIB PET brain imaging for the diagnosis of AD and other dementias [64]. The researchers evaluated the clinical utility of these neuroimaging techniques in enhancing diagnostic accuracy and differentiating AD from other forms of dementia. A cohort of patients exhibiting cognitive impairments was included in the study. They underwent ¹⁸FDG PET and ¹¹C-PIB PET brain imaging in conjunction with standard clinical assessments. The imaging results were compared to the final clinical diagnosis to determine the impact of PET scans on diagnostic outcomes. The results of the study revealed a significant diagnostic impact of both ¹⁸FDG PET and ¹¹C-PIB PET brain imaging in the memory clinic setting for AD and other dementias. ¹⁸FDG PET provided valuable insights into brain metabolism, enabling differentiation between AD and non-AD dementias. Conversely, ¹¹C-PIB PET identified the presence of amyloid plaques, a hallmark of AD pathology, thereby contributing to the diagnosis of AD [64].

Visualizing amyloid and tau pathology: positron emission tomography imaging

Among various neuroimaging techniques, PET stands out as an important tool, harnessing the power of molecular neuroimaging to target specific molecular markers linked to the pathology of the specific neurological diseases. In the case of AD, PET imaging employs radioligands specific to Aβ and tau protein deposits, revolutionizing our understanding of disease pathogenesis [65]. Aβ PET imaging allows for the in vivo detection and quantification of amyloid plaques, a hallmark feature of AD, making it instrumental for early diagnosis and disease progression monitoring. This technique aids in distinguishing AD from other dementias and identifying individuals with preclinical AD. Tau PET imaging complements Aβ imaging by capturing the distribution of neurofibrillary tangles, the second hallmark of AD. This modality provides insights into the spatiotemporal progression of tau pathology, correlating with cognitive decline [66]. PET-based measures of Aβ and tau burden offer a comprehensive assessment of the underlying molecular processes, guiding treatment evaluation in clinical trials and facilitating the development of targeted interventions [67]. Integrating Aβ and tau PET imaging with clinical assessments enhances diagnostic accuracy, leading to personalized diagnostic approaches and therapies [68, 69].

Meanwhile, single-photon emission computed tomography (SPECT) emerges as a cornerstone in the comprehensive assessment and understanding of AD. As a non-invasive imaging technique, SPECT plays a pivotal role in evaluating cerebral blood flow and functional activity in individuals affected by AD. This capability is particularly crucial for early and accurate diagnosis, given its unique ability to offer complementary information to structural imaging methods. SPECT’s distinctive contribution lies in its power to differentiate AD from other forms of dementia, enhancing diagnostic precision and guiding clinicians in tailoring interventions to the specific needs of each patient.

Beyond its diagnostic prowess, SPECT serves as a critical asset in longitudinal studies, enabling researchers to delve into the dynamic changes in cerebral perfusion patterns over time. This longitudinal perspective not only contributes significantly to our understanding of the progressive nature of AD but also provides invaluable data for the development of therapeutic strategies and personalized patient care plans. In the evolving landscape of neuroimaging, SPECT stands as an indispensable tool, offering both accessibility and depth of insights that are vital for advancing our knowledge of AD and, ultimately, improving clinical outcomes for individuals affected by this complex neurodegenerative condition.

Neuroimaging techniques constitute indispensable tools in both clinical and research domains, affording non-invasive insights into the intricate structures and dynamic functions of the human brain. These methodologies, however, exhibit varying capabilities contingent upon their inherent spatial and temporal resolutions, magnetic field strengths, and particular structural targets [70]. In this exposition, a comparative table (Table 1) elucidate multiple neuroimaging modalities, delineating their spatial and temporal resolutions, conventional magnetic field strengths, and principal applications. A short guide which helps to choose the right brain imaging method that best suits according to the specific investigation needs [71].

Table 1

Comparative table elucidating multiple neuroimaging properties

Neuroimaging Modality	Spatial Resolution	Typical Field Strength	Structure Detected	Application	Reference
Structural MRI	(sub-millimeter to millimeter scale)	1.5T to 7T	Detailed anatomical features including the cortex, white matter, subcortical structures, and lesions.	Anatomical imaging for diagnosis, treatment planning, and research in various medical specialties.	[72]
Diffusion Tensor Imaging (DTI)	Moderate	3T to 7T	White matter tracts and connectivity by measuring water diffusion in axonal fibers.	Investigating white matter pathways, connectivity, and microstructure in the brain.	[73]
Positron Emission Tomography (PET)	Moderate to Low (a few millimeters)	–	Metabolic processes, receptor densities, and functional activity, particularly in deep brain regions.	Imaging metabolic activity, receptor densities, and function in neurology, oncology, and psychiatry.	[74]
Single-Photon Emission Computed Tomography (SPECT)	Moderate to Low (a few millimeters)	–	Blood flow, metabolic activity, and receptor densities in various brain regions.	Evaluating blood flow, perfusion, and functional abnormalities in neurological and psychiatric disorders.	[75]
Computed Tomography (CT)		–	Brain atrophy, notably in the hippocampus and cortical areas, indicating volume loss.	Early stages of diagnostic evaluation to rule out other conditions.

INTERCONNECTED INSIGHTS: NEUROIMAGING, COMORBIDITIES, AND ALZHEIMER’S DISEASE MANAGEMENT

The contemporary landscape of medical investigation has undergone a profound metamorphosis through the integration of advanced neuroimaging modalities, catalyzing an epochal transformation in the ability to elucidate and discern the nascent precursors heralding the advent of AD. This intricate neurodegenerative phenomenon, renowned for its multifaceted pathophysiological intricacies and consequential societal perturbations, has come under heightened scrutiny, facilitated by the convergence of three distinct echelons of neuroimaging techniques. Embarking upon a comprehensive exploration of structural, functional, and molecular imaging domains, the pivotal role of sensitivity in decoding and comprehending the early manifestations of AD pathology assumes paramount import. This sensitivity not only fosters heightened aptitude in the identification of incipient indications but also underpins the orchestration of prompt interventions and tailored therapeutic modalities. In the subsequent discourse, the intricate dimensions and nuances of these neuroimaging categories are navigated, transcending the respective strengths and limitations [76].

The progressive evolution of neuroimaging methodologies has yielded a marked augmentation in sensitivity, underscoring their significance in the early stages of neurodegenerative detection. Augmenting magnetic field intensities, exemplified by 3T and 7T MRI systems, has engendered escalated signal-to-noise ratios, thus imbuing heightened discernment of nuanced anatomical adjustments [77]. In tandem, the assimilation of innovative pulse sequences and parallel imaging paradigms has fortified signal precision and spatial resolution enhancements, quantifiable within the bracket of 20–40% [78]. The scope of PET has been revitalized by the emergence of dedicated radiotracers, as evidenced by amyloid tracers engendering augmented sensitivities of 20–30% compared to their precursors [79]. fMRI has undergone efficacy augmentation through refined spatial resolutions that delve beneath the sub-millimeter range. This is paralleled by multiband acquisition strategies, culminating in expedited image capture, scaling by factors of 2–8 [80]. SPECT, propelled by the integration of high-resolution collimators and hybrid configurations, epitomizes sensitivity enhancement, with spatial resolution enhancement estimated between 1.5 to 2 times. The ascendancy of DTI lies in harnessing high-angular resolution diffusion imaging methodologies, which hone the discernment of intricate white matter topographies. The fusion of PET-MRI or PET-CT within hybrid frameworks begets a harmonized juxtaposition of functional and anatomical attributes.

Recent scientific investigations have illuminated the intricate interplay between AD and concurrent comorbid conditions. Notably, cardiovascular issues like hypertension and cardiac pathologies amplify neurodegenerative processes through compromised cerebral blood flow. Metabolic dysregulation, as seen in diabetes, intricately intersects with AD neuropathology, hastening cognitive decline. Several studies have delved into these interactions. The research conducted by Marsegila et al., delves into the impact of prediabetes and diabetes on cognitive decline and their ability to predict microvascular lesions. The population-based cohort study demonstrated a link between prediabetes, diabetes, and faster cognitive decline highlighting their conceivable implications for microvascular pathogenesis [81]. Similar results were obtained by a study which aims to uncover potential interrelations between these two complex and prevalent diseases by analyzing various biological information sources [82]. The researchers used computational methods to predict molecular links, shedding light on possible connections and shared mechanisms between AD and diabetes mellitus [83]. The study employed machine learning techniques to identify crucial microRNAs that may play a significant role in the connection between these two conditions. The researchers observed miR-103 as a potential modulator for type 2 diabetes mellitus (T2DM), obesity, cardiovascular diseases, and AD. The findings of this research highlighted the promising targets for therapeutic interventions and underscore the significance of miRNAs as diagnostic biomarkers for T2DM and AD risk.

Affective disorders like depression complicate the bidirectional relationship between mood and cognition. Sleep disturbances, such as insomnia and sleep apnea, disrupt cognitive function. Okuda et al. [75] explores the connection between sleep disturbances in AD patients and their caregivers’ burden and health status. The study involved the examination of 496 caregivers of AD patients with insomnia symptoms which revealed a significant positive association between the caregivers’ burden, by the BIC-11 total score, and the severity of sleep disturbances in AD patients measured by the Sleep Disorders Inventory score. Moreover, the study identified a correlation between sleep disruptions in AD patients and various aspects of caregivers’ health, encompassing sleep quality, depression levels, and physical/mental quality of life. The outcomes of this study underline the interconnectedness of sleep disturbances, caregiver burden, and the well-being of both AD patients and their caregivers. The convergence of Parkinson’s disease and AD presents complex clinical phenotypes, while vascular anomalies like small vessel disease contribute to cognitive decline. Musculoskeletal frailties and respiratory conditions add further layers of complexity. Thyroid abnormalities, obesity, and chronic kidney disease impact cognitive status [84 –86]. Thus, recognizing the interconnectedness of these comorbidities and their integral role in AD management necessitates a comprehensive approach.

ADVANCING NEUROIMAGING THROUGH THE SYNERGY OF ARTIFICIAL INTELLIGENCE AND DEEP LEARNING

The advent of AI approaches in the field of neuroimaging has emerged as a response to the limitations inherent within the conventional methods. Traditional neuroimaging techniques often rely on manual analysis, which is time-consuming, subjective, and prone to inter-observer variability [87]. Moreover, the complexity and high dimensionality of neuroimaging data present challenges for traditional analytical approaches to effectively extract meaningful information. Table 2 below provides a comparative analysis of different neuroimaging techniques.

Table 2

Comparative analysis of different neuroimaging techniques

Neuroimaging Technique	CT	MRI	PET	SPECT
Type	Structural	Structural	Functional	Function
Spatial resolution	Low	Good	Good/ Excellent	Good
Radioactivity	Yes	No	Yes	Yes
Temporal Resolution	Single time-point	Single time-point	Poor	Poor
Measures	Density of Tissue	Detailed images of the brain’s anatomy	Glucose Metabolism	CBF response
Challenges	Insufficient clarity in visualizing the structures located in the posterior fossaLimited to assessing anatomical features exclusively.	Artifacts resulting from the presence of nonferromagnetic metallic objectsLimited to measuring anatomical features exclusively.	Blood flow imposes limitations on the resolution of the imagingA separate session is necessary to obtain structural MRI data.	The resolution of the imaging is restricted by the influence of blood flowAcquisition of structural MRI data necessitates a separate session.

Considering these drawbacks, researchers have turned to AI, particularly deep learning, as a powerful tool for addressing these limitations and advancing the field of neuroimaging as represented in Fig. 2.

Fig. 2

Schematic representation integrating artificial intelligence and deep learning for neuroimaging.

The utilization of AI, specifically deep learning, offers a compelling avenue for surmounting these challenges. Deep learning algorithms have the capacity to autonomously acquire hierarchical representations directly from unprocessed neuroimaging data, facilitating the extraction of intricate features and patterns that might elude human observers. Through the integration of extensive multimodal datasets, deep learning models have the potential to unveil nuanced relationships between neuroimaging biomarkers and the progression of various diseases [88]. As a result, AI-based approaches in neuroimaging hold significant potential for enhancing diagnostic accuracy and efficiency. Deep learning models have exhibited remarkable efficacy and performance in diverse tasks, notably in disease classification, lesion detection, and image segmentation, surpassing traditional methods. Leveraging extensive labeled datasets, these models acquire the capability to generalize their knowledge and accurately predict outcomes for unseen cases [89].

Recent scientific breakthroughs in AD research have showcased the synergistic potential of AI and machine learning algorithms with digital biomarkers, leading to a significantly enhanced predictive power. These cutting-edge technologies can efficiently analyze vast datasets, discern complex patterns, and provide more accurate predictions of disease progression. Furthermore, they contribute significantly to the development of personalized therapeutic strategies by gaining a deeper understanding of individual responses to medications and interventions [90].

In the realm of AD research, machine learning (ML) tasks encompass a diverse set of crucial objectives and analyses. Several prominent ML tasks within AD research include:

Disease classification

To effectively discriminate individuals with AD or those at elevated risk of AD development, extensive research has been dedicated to classifying AD patients from individuals with MCI and healthy controls [59]. Classification endeavors in the AD domain commonly utilize diverse imaging techniques, including MRI, PET, and EEG [91]. In the past, traditional ML techniques like support vector machines, multi-layer perceptrons, and convolutional neural networks were commonly employed for AD classification. These methods typically achieved classification accuracies of approximately 90% [92]. Beyond classification accuracy, recent AI models demonstrate significant capabilities in tasks like predicting disease progression trajectories, identifying novel biomarkers for early detection, and providing personalized treatment recommendations. Highlighting the measurable performance in these additional tasks provides a more detailed perspective on the comprehensive impact of AI in the realm of AD, showcasing its potential to contribute meaningfully to various aspects of patient care and research.

In response to the significant progress in deep learning methodologies, researchers have initiated the application of these techniques to classify imaging data for the diagnosis of AD.

Progression prediction

Progression prediction refers to the ability of machine learning models to forecast how the disease will advance in individuals over time. These models utilize diverse data sources such as longitudinal neuroimaging data, genetic information, and clinical records. By analyzing this information, ML algorithms offer valuable insights into the patterns of disease progression and can identify individuals at high risk of developing AD, thereby enabling early interventions that may be more beneficial [93].

Predicting disease progression in AD encompasses two distinct tasks. The first task involves identifying individuals with MCI or normal cognition who have a higher likelihood of converting to AD [94]. For instance Rye et al., investigated the prediction of AD trajectory in individuals initially diagnosed with MCI using data from the AD Neuroimaging Initiative database [95]. The authors used a cohort study which was divided into stable MCI (n = 357) and MCI individuals progressing to AD (n = 321). Employing machine learning with cognitive function, hippocampal volume, and APOE genetic status as features, two independent classification methods consistently achieved an accuracy of around 70% across different validation procedures. Through the study it could suggested that the potential development of tools can assist clinicians in making prognostic decisions. Minhas et al. employed a decision support technique, integrating MRI-based biomarkers and neuropsychological measures, to identify those at risk of transitioning from MCI to AD. Using a novel method for disease progression, the study achieved a high classification AUC of 91.2% and 95.7% for predicting AD 6 months and 1 year ahead, respectively, with multimodal markers [96].

Another task entails predicting longitudinal AD-related scores, which is a more intricate challenge due to the availability of longitudinal measurements. Methods that involve deep learning have been employed to tackle the complexity associated with longitudinal measurements. These include conditional restricted Boltzmann machine, recurrent neural network, and ensemble models based on stacked convolutional neural network and bidirectional long short-term memory network [90].

Moreover, another study investigates the utilization of a Digital Neuro Signature (DNS) to assess longitudinal individual-level changes in AD [97]. The study focuses on the efficiency of DNS in measuring cognitive alterations over time, emphasizing its potential as a valuable tool for tracking disease progression and individual responses. The study evaluated Altoida’s DNS, a digital cognitive test, against conventional neuropsychological assessments for AD detection. DNS proved more efficient (10 min versus 45–120 min), with higher test-retest reliability and accuracy in detecting abnormal cognition. Two observational experiments with 525 participants demonstrated DNS’s consistent sensitivity in capturing individual-level cognitive changes compared to conventional assessments, especially in advanced disease stages. Dispersion differences were more pronounced with disease progression, and DNS intraindividual variability predicted conversion from MCI to AD. These findings have implications for patient monitoring, remote clinical trials, and timely interventions in AD management [97].

Image segmentation

Image segmentation using ML algorithms plays a crucial role in accurately delineating brain regions affected by AD pathology. By precisely segmenting brain images, ML algorithms enable quantitative assessment and monitoring of disease-related changes in neuroimaging data [98].

The process of image segmentation involves dividing an image into distinct regions or objects. In the context of AD, the aim is to identify and delineate specific brain regions that are indicative of AD pathology, such as areas of atrophy, the presence of amyloid plaques, or alterations in connectivity patterns [99]. ML algorithms are trained on labeled datasets, where experts have manually annotated the regions of interest.

Once trained, ML algorithms can automatically segment brain images with a high degree of accuracy and consistency, eliminating the subjectivity and potential inter-observer variability associated with manual segmentation. This precise image segmentation allows for quantitative analysis of disease-related changes over time and provides valuable insights into the progression and severity of AD [100].

Accurate segmentation of brain regions affected by AD pathology facilitates various applications in AD research and clinical practice. For instance, it enables the measurement of specific biomarkers associated with disease progression, such as hippocampal volume or amyloid plaque burden. This quantitative information can aid in the early detection of AD, tracking disease progression, and evaluating treatment efficacy [101].

Furthermore, image segmentation assists in characterizing disease subtypes by identifying specific patterns of neurodegeneration or identifying regions of interest that are predictive of disease conversion from MCI to AD. It also facilitates the assessment of treatment response by quantifying changes in segmented regions following therapeutic interventions.

Thus, AI methods present an opportunity to uncover novel insights and biomarkers in neuroimaging. Through harnessing the computational power of deep learning, researchers are being able to identify previously unidentified imaging markers and unveil new imaging signatures that are associated with disease progression, subtypes, or treatment response.

Despite the significant advancements in the integration of AI and deep learning for neuroimaging, several challenges persist. The availability of large and diverse datasets, encompassing comprehensive neuroimaging features and associated clinical information, remains a critical obstacle due to time, cost, and ethical considerations related to patient privacy. Another significant challenge lies in the interpretability and transparency of AI models, as deep learning algorithms often operate as opaque black boxes, necessitating the development of interpretable models for establishing trust and acceptance. Standardization and harmonization of imaging protocols and analysis methods across different sites and scanners present difficulties, as variations can significantly impact the performance and generalizability of AI models. Furthermore, extensive validation and rigorous evaluation through robust clinical studies are imperative to assess the accuracy and clinical utility of AI-driven neuroimaging techniques, while simultaneously addressing regulatory and ethical concerns surrounding model interpretability, patient consent, and potential biases.

ADVANCED TECHNOLOGICAL APPLICATIONS FOR ALZHEIMER’S DISEASE ASSESSMENT AND CARE: WEARABLES, SMARTPHONES, AND BEYOND

The integration of wearable technology into the forefront of AD research and clinical management epitomizes a highly promising development with substantial potential. Wearable technologies based on EEG and functional near-infrared spectroscopy hold the capacity to significantly enhance early diagnostic capabilities, ameliorate patient care delivery, and elevate the overall quality of life for both affected individuals and their caregivers [102, 103]. As technological advancements continue to unfold and ethical paradigms evolve, wearable devices are positioned as indispensable instruments in the multifaceted arsenal deployed in the concerted endeavor to combat AD, thereby providing a beacon of hope within the intricate landscape of neurodegenerative disorders as represented in Fig. 3 [104, 105].

Fig. 3

Schematic representation of advancing Alzheimer’s disease research and care through smartphone technology.

The reservoir of longitudinal data generated by these wearable devices holds substantial scientific merit. These datasets have the potential to yield insights into prospective biomarkers relevant to AD, elucidate the intricate dynamics underpinning disease progression, and facilitate the rigorous evaluation of therapeutic modalities. Numerous scientific investigations have provided substantial corroborating evidence in alignment with the current research. For instance, Skirrow et al., study explored the utility of AI systems that leveraged speech and language patterns to facilitate the early detection of AD. The study, involved 133 participants with established Aβ biomarker status and clinical profiles, employed daily story recall tasks administered via smartphones and analyzed the data using an AI system. The findings revealed that the AI system demonstrated promising capabilities in predicting MCI and mild AD with a high degree of accuracy (area under the curve [AUC] = 0.85). Conducting sub-analyses within clinical groups, such as MCI/mild AD and cognitively unimpaired individuals, showed improved prediction of Aβ status (AUC = 0.78 and AUC = 0.74, respectively) when using short story variants for immediate recall. As a result, the study suggested that AI-driven assessments of daily story recall tasks through smartphone applications hold great promise for predicting MCI and mild AD, potentially enabling early detection. However, while the AI system demonstrated relatively good predictive performance for Aβ status in clinical subgroups, its accuracy in the broader population was more limited [106 –108].

Similarly, the Stavropoulos et al. study aimed to assess the feasibility and acceptability of using wearable monitoring devices among individuals with AD and their caregivers. It sought to understand their preferences, priorities, and concerns regarding these technologies, with the intention of informing device selection for the RADAR-AD project. The study involved a Public Involvement activity with the Patient Advisory Board, which includes individuals with dementia and their caregivers. The key findings throughout the study revealed a general willingness among participants to incorporate wearable devices into their daily lives, driven by considerations of comfort, convenience, and affordability. Participants emphasized factors like device appearance, battery life, and water resistance, along with price, the availability of an emergency button, and the presence of a screen displaying metrics. Among the provided metrics, activity levels and heart rate were considered the most valuable, followed by respiration rate, sleep quality, and distance. These AI-powered personalized data metrics along with the neuroimaging data help in augmented patient care. However, concerns included device complexity, the risk of forgetting to charge the device, worries about potential social stigma, and data privacy issues [109].

CONCLUSION

The progress in neuroimaging technology for AD enables the identification of clinical or pathological changes over time. Neuroimaging techniques play a significant role in both research and clinical applications. The advancements in structural and functional neuroimaging allow for the early detection of AD, even years before the onset of dementia symptoms. A recent notable breakthrough is the development of Aβ and tau imaging techniques, which enable the identification of their deposits in the brain through in vivo imaging. Longitudinal studies utilizing structural and functional imaging methods are currently considered the most reliable in evaluating the progressive decline in individuals with MCI and AD. Moreover, the integration of AI and deep learning techniques has significantly advanced the field of neuroimaging. These approaches have opened new possibilities for analyzing complex neuroimaging data and extracting valuable insights related to AD. By utilizing AI algorithms, researchers have been able to explore and identify neuroimaging biomarkers that unravel the underlying pathology and progression of AD. This integration enables the accurate detection and classification of AD, providing early diagnostic information and aiding in the understanding of disease mechanisms. Additionally, AI-driven neuroimaging techniques have the potential to improve prediction models for disease progression and facilitate personalized treatment strategies. The synergistic combination of AI neuroimaging approaches holds immense promise in transforming our understanding of AD and has the potential to advance the development of improved diagnostic tools and therapeutic strategies.

AUTHOR CONTRIBUTIONS

Maudlyn O. Etekochay (Conceptualization; Writing – original draft; Writing – review & editing); Amoolya Rao Amaravadhi (Writing – original draft; Writing – review & editing); Gabriel Villarrubia González (Writing – original draft; Writing – review & editing); Atanas G. Atanasov (Writing – original draft; Writing – review & editing); Maima Matin (Writing – original draft; Writing – review & editing); Mohammad Mofatteh (Writing – original draft; Writing – review & editing); Harry Wilhelm Steinbusch (Writing – original draft); Tadele Tesfaye (Writing – review & editing); Domenico Pratico (Conceptualization; Supervision; Writing – review & editing).

Footnotes

ACKNOWLEDGMENTS

Domenico Praticò is the Scott Richards North Star Charitable Foundation Chair for Alzheimer’s disease research.

FUNDING

The authors have no funding to report.

CONFLICT OF INTEREST

Domenico Praticò is an Editorial Board Member of this journal but was not involved in the peer-review process nor had access to any information regarding the peer-review.

The authors have no conflict of interest to disclose.

DATA AVAILABILITY

Data sharing is not applicable to this article as no datasets were generated or analyzed during this study.

References

Breijyeh

, Karaman

(2020) Comprehensive review on Alzheimer’s disease: Causes and treatment. Molecules 25, 5789.

(2020) Alzheimer’s disease facts and figures. Alzheimers Dement 16, 391–460.

Serrano-Pozo

, Frosch

, Masliah

, Hyman

(2011) Neuropathological alterations in Alzheimer disease, Cold Spring Harb Perspect Med 1, a006189.

Arunsundar

, Shanmugarajan

, Ravichandran

(2015) 3,4-dihydroxyphenylethanol attenuates spatio-cognitivedeficits in an Alzheimer’s disease mouse model: Modulation of the molecular signals in neuronalsurvival-apoptotic programs. Neurotox Res 27, 143–155.

DiBattista

, Heinsinger

, Rebeck

(2016) Alzheimer’s disease genetic risk factor APOE-ɛ4 also affects normal brain function. Curr Alzheimer Res 13, 1200–1207.

Kim

, Jeong

, Stiles

, Choi

(2022) Neuroimaging modalities in Alzheimer’s disease: Diagnosis and clinical features. Int J Mol Sci 23, 6079.

Agosta

, Galantucci

, Filippi

(2017) Advanced magnetic resonance imaging of neurodegenerative diseases. Neurol Sci 38, 41–51.

Klyucherev

, Olszewski

, Shalimova

, Chubarev

, Tarasov

, Attwood

, Syvänen

, Schiöth

(2022) Advances in the development of new biomarkers for Alzheimer’s disease. Transl Neurodegener 11, 25.

Khoury

, Ghossoub

(2019) Diagnostic biomarkers of Alzheimer’s disease: A state-of-the-art review. Biomark Neuropsychiatry 1, 100005.

10.

Suppiah

, Didier

, Vinjamuri

(2019) The who, when, why, and how of PET amyloid imaging in management of Alzheimer’s disease—review of literature and interesting images. Diagnostics 9, 65.

11.

Zhang

, Chen

, Li

, Sterling

, Song

(2023) Amyloid β-based therapy for Alzheimer’s disease:Challenges, successes, and future. Sig Transduct Target Ther 8, 248.

12.

Cummings

, Zhou

, Lee

, Zhong

, Fonseca

, Cheng

(2023) Alzheimer’s disease drug development pipeline: 2023, Alzheimers Dement (N Y) 9, e12385.

13.

MohanaSundaram

, Sathanantham

, Lukas Sveikata

, Lalla

, Sveikatien·eq

, Kaushik

, Velayutham

(2022) Aducanumab and adenoviral COVID-19 vaccines: Increased cerebral hemorrhage risk ? Expert RevNeurother 22, 283–286.

14.

Lowe

, Willis

, Hawdon

, Natanegara

, Chua

, Foster

, Shcherbinin

, Ardayfio

, Sims

(2017) Donanemab(LY3002813) dose-escalation study in Alzheimer’s disease, Alzheimers Dementia 7, e12112.

15.

Bateman

, Cummings

, Schobel

, Salloway

, Vellas

, Boada

, Black

, Blennow

, Fontoura

, Klein

, Assunção

, Smith

, Doody

(2022) Gantenerumab: An anti-amyloid monoclonal antibody with potential disease-modifying effects in early Alzheimer’s disease. Alzheimers Res Ther 14, 178.

16.

Izzo

, Yuede

, LaBarbera

, Limegrover

, Rehak

, Yurko

, Waybright

, Look

, Rishton

, Safferstein

, Hamby

, Williams

, Sadlek

, Edwards

, Davis

, Grundman

, Schneider

, DeKosky

, Chelsky

, Pike

, Henstridge

, Blennow

, Zetterberg

, LeVine

, Spires-Jones

, Cirrito

, Catalano

(2021) Preclinical andclinical biomarker studies of CT1812: A novel approach to Alzheimer’s disease modification. AlzheimersDement 17, 1365–1382.

17.

Haug

, Drazen

(2023) Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med 388, 1201–1208.

18.

Atanasov

(2023) Exploration of digital health technologies. Explor Digit Health Technol 1, 1–3.

19.

Crous-Bou

, Minguillón

, Gramunt

, Molinuevo

(2017) Alzheimer’s disease prevention: From risk factors to early intervention. Alzheimers Res Ther 9, 71.

20.

Buckner

, Snyder

, Shannon

, LaRossa

, Sachs

, Fotenos

, Sheline

, Klunk

, Mathis

, Morris

, Mintun

(2005) Molecular, structural, and functional characterization of Alzheimer’s disease: Evidence for a relationship between default activity, amyloid, and memory. J Neurosci 25, 7709–7717.

21.

Bigler

(2015) Structural image analysis of the brain in neuropsychology using magnetic resonance imaging (MRI) techniques. Neuropsychol Rev 25, 224–249.

22.

Frisoni

, Fox

, Jack

, Scheltens

, Thompson

(2010) The clinical use of structural MRI in Alzheimer disease. Nat Rev Neurol 6, 67–77.

23.

Hutton

, De Vita

, Ashburner

, Deichmann

, Turner

(2008) Voxel-based cortical thickness measurements in MRI. Neuroimage 40, 1701–1710.

24.

Lehmann

, Crutch

, Ridgway

, Ridha

, Barnes

, Warrington

, Rossor

, Fox

(2011) Cortical thickness and voxel-based morphometry in posterior cortical atrophy and typical Alzheimer’s disease. Neurobiol Aging 32, 1466–1476.

25.

Rao

, Ganaraja

, Murlimanju

, Joy

, Krishnamurthy

, Agrawal

(2022) Hippocampus and its involvement in Alzheimer’s disease: A review. 3 Biotech 12, 55.

26.

Ridgway

, Omar

, Ourselin

, Hill

DLG

, Warren

, Fox

(2009) Issues with threshold masking in voxel-based morphometry of atrophied brains. Neuroimage 44, 99–111.

27.

Bigham

, Zamanpour

, Zemorshidi

, Boroumand

, Zare

(2020) Identification of superficial white matter abnormalities in Alzheimer’s disease and mild cognitive impairment using diffusion tensor imaging. J Alzheimers Dis Rep 4, 49–59.

28.

Esrael

SMA

, Hamed

AMM

, Khedr

, Soliman

(2021) Application of diffusion tensor imaging in Alzheimer’sdisease: Quantification of white matter microstructural changes. Egypt J Radiol Nucl Med 52, 89.

29.

Alm

, Bakke

(2019) Relationships between diffusion tensor imaging and cerebrospinal fluid metrics in early stages of the Alzheimer’s disease continuum. J Alzheimers Dis 70, 965–981.

30.

Mayo

, Mazerolle

, Ritchie

, Fisk

, Gawryluk

(2017) Longitudinal changes in microstructural white matter metrics in Alzheimer’s disease. Neuroimage Clin 13, 330–338.

31.

Chang

, Chao

, Hsu

, Chen

, Tseng

WYI

(2021) White matter network disruption and cognitive correlatesunderlying impaired memory awareness in mild cognitive impairment. Neuroimage Clin 30, 102626.

32.

Bollmann

, Barth

(2021) New acquisition techniques and their prospects for the achievable resolution of fMRI. Prog Neurobiol 207, 101936.

33.

Glover

(2011) Overview of functional magnetic resonance imaging. Neurosurg Clin N Am 22, 133–139.

34.

, Yuan

(2015) PET/SPECT molecular imaging in clinical neuroscience: Recent advances in the investigationof CNS diseases. Quant Imaging Med Surg 5, 433–447.

35.

Chen

(2019) Functional MRI of brain physiology in aging and neurodegenerative diseases. Neuroimage 187, 209–225.

36.

Kim

, Ogawa

(2012) Biophysical and physiological origins of blood oxygenation level-dependent fMRI signals. J Cereb Blood Flow Metab 32, 1188–1206.

37.

Farràs-Permanyer

, Guàrdia-Olmos

, Peró-Cebollero

(2015) Mild cognitive impairment and fMRI studies of brain functional connectivity: The state of the art. Front Psychol 6, 1095.

38.

Zhu

, Risacher

, Wishart

, Saykin

(2023) Functional MRI studies of memory in aging, mild cognitive impairment, and Alzheimer’s disease. In Functional Neuroradiology: Principles and Clinical Application, Faro SH, Mohamed FB, eds. Springer International Publishing, Cham, pp. 671-712.

39.

Corriveau-Lecavalier

, Mellah

, Clément

, Belleville

(2019) Evidence of parietal hyperactivation in individuals with mild cognitive impairment who progressed to dementia: A longitudinal fMRI study. Neuroimage Clin 24, 101958.

40.

Brakowski

, Spinelli

, Dörig

, Bosch

, Manoliu

, Holtforth

, Seifritz

(2017) Resting state brainnetwork function in major depression –Depression symptomatology, antidepressant treatment effects, futureresearch. J Psychiatr Res 92, 147–159.

41.

Dennis

, Thompson

(2014) Functional brain connectivity using fMRI in aging and Alzheimer’s disease. Neuropsychol Rev 24, 49–62.

42.

Holiga

, Sambataro

, Luzy

, Greig

, Sarkar

, Renken

, Marsman

, Schobel

, Bertolino

, Dukart

(2018) Test-retest reliability of task-based and resting-state blood oxygen level dependence and cerebral blood flow measures, PLoS One 13, e0206583.

43.

Zhao

, Makowski

, Hagler

, Garavan

, Thompson

, Greene

, Jernigan

, Dale

(2023) Task fMRI paradigms may capture more behaviorally relevant information than resting-state functional connectivity. Neuroimage 270, 119946.

44.

Young

PNE

, Estarellas

, Coomans

, Srikrishna

, Beaumont

, Maass

, Venkataraman

, Lissaman

, Jiménez

, Betts

, McGlinchey

, Berron

, O’Connor

, Fox

, Pereira

, Jagust

, Carter

, Paterson

, Schöll

(2020) Imaging biomarkers in neurodegeneration: Current and future practices. Alzheimers Res Ther 12, 49.

45.

Devanand

, Mikhno

, Pelton

, Cuasay

, Pradhaban

, Kumar

JSD

, Upton

, Lai

, Gunn

, Libri

, Liu

, van Heertum

, Mann

, Parsey

(2010) Pittsburgh compound B (11C-PIB) and fluorodeoxyglucose (18F-FDG) PET in patients with Alzheimer’s disease, mild cognitive impairment, and healthy controls. J Geriatr Psychiatry Neurol 23, 185–198.

46.

Okamura

, Harada

, Ishiki

, Kikuchi

, Nakamura

, Kudo

(2018) The development and validation of tau PET tracers: Current status and future directions. Clin Transl Imaging 6, 305–316.

47.

Henriques

, Benedet

, Camargos

, Rosa-Neto

, Nóbrega

(2018) Fluid and imaging biomarkers for Alzheimer’s disease: Where we stand and where to head to. Exp Gerontol 107, 169–177.

48.

Chen

, Xu

, Yan

, Zhou

, Jiang

, Melcher

, Xu

(2017) Amyloid beta: Structure, biology and structure-based therapeutic development. Acta Pharmacol Sin 38, 1205–1235.

49.

Tosun

, Veitch

, Aisen

, Jack

Jr , Jagust

, Petersen

, Saykin

, Bollinger

, Ovod

, Mawuenyega

, Bateman

, Shaw

, Trojanowski

, Blennow

, Zetterberg

, Weiner

(2021) Detection of β-amyloid positivity in Alzheimer’s Disease Neuroimaging Initiative participants with demographics, cognition, MRI and plasma biomarkers, Brain Commun 3, fcab008.

50.

Rajmohan

, Reddy

(2017) Amyloid beta and phosphorylated tau accumulations cause abnormalities at synapses of Alzheimer’s disease neurons. J Alzheimers Dis 57, 975–999.

51.

Cohen

, Rabinovici

, Mathis

, Jagust

, Klunk

, Ikonomovic

(2012) Using Pittsburgh Compound B for In vivo PET imaging of fibrillar amyloid-beta, Adv Pharmacol 64, 27–81.

52.

Barbier

, Zejneli

, Martinho

, Lasorsa

, Belle

, Smet-Nocca

, Tsvetkov

, Devred

, Landrieu

(2019) Role of tau as a microtubule-associated protein: Structural and functional asects. Front Aging Neurosci 11, 204.

53.

Vacchi

, Kaelin-Lang

, Melli

(2020) Tau and alpha synuclein synergistic effect in neurodegenerative diseases: When the periphery is the core. Int J Mol Sci 21, 14.

54.

Tsai

, Bejanin

, Lesman-Segev

, LaJoie

, Visani

, Bourakova

, O’Neil

, Janabi

, Baker

, Lee

, Perry

, Bajorek

, Karydas

, Spina

, Grinberg

, Seeley

, Ramos

, Coppola

, Gorno-Tempini

, Miller

, Rosen

, Jagust

, Boxer

, Rabinovici

(2019) 18F-flortaucipir (AV-1451) tau PET in frontotemporal dementia syndromes. Alzheimers Res Ther 11, 13.

55.

Shin

, Lee

(2010) 2-(1-{6-[(2-[Fluorine-18]Fluoroethyl)(Methyl) Amino]-2-Naphthyl} Ethylidene)Malononitrile (FDDNP) positron emission tomography patterns in nondemented populations. Am J Geriatr Psychiatry 18, 187.

56.

Wang

, Jin

, Zhou

, Tian

, Lee

, Zhang

(2023) PET molecular imaging for pathophysiological visualization in Alzheimer’s disease. Eur J Nucl Med Mol Imaging 50, 765–783.

57.

James

, Doraiswamy

, Borges-Neto

(2015) PET Imaging of tau pathology in Alzheimer’s disease and tauopathies. Front Neurol 6, 38.

58.

, Zhang

, Zhou

, Zhang

, Xie

, Zhu

, Cui

, Wang

(2022) In vivo imaging of tau deposition in Alzheimer’s disease using both [18F]-THK5317 and [18F]-S16: A pilot human study. Front Aging Neurosci 14, 994750.

59.

Park

, Jang

, Kim

, Lee

, Moon

(2022) Promising blood biomarkers for clinical use in Alzheimer’s disease: A focused update. J Clin Neurol 18, 401–409.

60.

Knopman

, Haeberlein

, Carrillo

, Hendrix

, Kerchner

, Margolin

, Maruff

, Miller

, Tong

, Tome

, Murray

, Nelson

, Sano

, Mattsson

, Sultzer

, Montine

, Jack

Jr , Kolb

, Petersen

, Vemuri

, Canniere

, Schneider

, Resnick

, Romano

, van Harten

, Wolk

, Bain

, Siemers

(2018) The National Institute on Aging and the Alzheimer’s association research framework for Alzheimer’s disease: Perspectives from the research roundtable. Alzheimers Dementia 14, 563–575.

61.

Duran-Aniotz

, Hetz

(2016) Glucose metabolism: A sweet relief of Alzheimer’s disease, Curr Biol 26, R806–R809.

62.

Watts

, Pocock

, Claudianos

(2018) Brain energy and oxygen metabolism: Emerging role in normal function and disease. Front Mol Neurosci 11, 216.

63.

Choi

, Seo

, Kim

, Park

(2023) The Use of F-18 FDG PET-Based cognitive reserve to evaluate cognitive decline in Alzheimer’s disease, independent of educational influence. Medicina (Kaunas) 59, 945.

64.

Shea

, Ha

, Lee

, Chu

(2016) Impact of (18)FDG PET and (11)C-PIB PET brain imaging on the diagnosis of Alzheimer’s disease and other dementias in a regional memory clinic in Hong Kong. Hong Kong Med J 22, 327–333.

65.

Bao

, Jia

, Finnema

, Cai

, Carson

, Huang

(2017) PET Imaging for early detection of Alzheimer’s disease: From pathologic to physiologic biomarkers. PET Clinics 12, 329–350.

66.

Hampel

, Hardy

, Blennow

, Chen

, Perry

, Kim

, Villemagne

, Aisen

, Vendruscolo

, Iwatsubo

, Masters

, Cho

, Lannfelt

, Cummings

, Vergallo

(2021) The amyloid-β pathway in Alzheimer’s disease. Mol Psychiatry 26, 5481–5503.

67.

Brier

, Gordon

, Friedrichsen

, McCarthy

, Stern

, Christensen

, Owen

, Aldea

, Su

, Hassenstab

, Cairns

, Holtzman

, Fagan

, Morris

, Benzinger

, Ances

(2016) Tau and Aβ imaging, CSF measures, and cognition in Alzheimer’s disease, Sci Transl Med 8, 338ra66.

68.

Ayubcha

, Rigney

, Borja

, Werner

, Alavi

(2021) Tau-PET imaging as a molecular modality for Alzheimer’s disease. Am J Nucl Med Mol Imaging 11, 374–386.

69.

Pemberton

, Collij

, Heeman

, Bollack

, Shekari

, Salvadó

, Lopes Alves

, Vallez Garcia

, Battle

, Buckley

, Stephens

, Bullich

, Garibotto

, Barkhof

, Gispert

, Farrar

(2022) Quantification of amyloid PET for future clinical use: A state-of-the-art review. Eur J Nucl Med Mol Imaging 49, 3508–3528.

70.

Lystad

, Pollard

(2009) Functional neuroimaging: A brief overview and feasibility for use in chiropractic research. J Can Chiropr Assoc 53, 59–72.

71.

Crosson

, Ford

, M cGregor

, Meinzer

, Cheshkov

, Li

, Walker-Batson

, Briggs

(2010) Functional imaging and related techniques: An introduction for rehabilitation researchers, J Rehabil Res Dev 47, vii–xxxiv.

72.

van der Kolk

, Hendrikse

, Zwanenburg

, Visser

, Luijten

(2013) Clinical applications of 7T MRI in the brain. Eur J Radiol 82, 708–718.

73.

Deppe

, Duning

, Mohammadi

, Schwindt

, Kugel

, Knecht

, Ringelsteinet

(2007) Diffusion-tensor imaging at 3 T: Detection of white matter alterations in neurological patients on the basis of normal values. Invest Radiol 42, 338–345.

74.

Lang

, Habs

, Parodi

, Thirolf

(2014) Sub-millimeter nuclear medical imaging with high sensitivity in positron emission tomography using β+γ coincidences, J Inst 9, P01008.

75.

Dorbala

, Ananthasubramaniam

, Armstrong

, Chareonthaitawee

, DePuey

, Einstein

, Gropler

, Holly

, Mahmarian

, Park

, Polk

, Russell 3rd

, Slomka

, Thompson

, Wells

(2018) Single photon emission computed tomography (SPECT) myocardial perfusion imaging guidelines: Instrumentation, acquisition, processing, and interpretation. J Nucl Cardiol 25, 1784–1846.

76.

Wollman

, Prohovnik

(2003) Sensitivity and specificity of neuroimaging for the diagnosis of Alzheimer’s disease. Dialogues Clin Neurosci 5, 89–99.

77.

Solomon

, Patriat

, Braun

, Palnitkar

, Moeller

, Auerbach

, Ugurbil

, Sapiro

, Harel

(2023) Motion robust magnetic resonance imaging via efficient Fourier aggregation. Medical Image Analysis 83, 102638.

78.

Schneider

(2017) MRI. In Small Animal Imaging: Basics and Practical Guide, Kiessling F, Pichler BJ, Hauff P, eds. Springer International Publishing, Cham, pp. 219-230.

79.

Perani

(2014) FDG-PET and amyloid-PET imaging: The diverging paths. Curr Opin Neurol 27, 405.

80.

Mellema

, Treacher

, Nguyen

, Montillo

(2019) Multiple deep learning architectures achieve superior performance diagnosing autism spectrum disorder using features previously extracted from structural and functional MRI. In IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019, pp. 1891-1895.

81.

Marseglia

, Fratiglioni

, Kalpouzos

, Wang

, Bäckman

, Xu

(2019) Prediabetes and diabetes accelerate cognitive decline and predict microvascular lesions: A population-based cohort study. Alzheimers Dement 15, 25–33.

82.

Castillo-Velázquez

, Martínez-Morales

, Castañeda-Delgado

, García-Hernández

, Herrera-Mayorga

, Paredes-Sánchez

, Rivera

, Rivas-Santiago

, Lara-Ramírez

(2023) Bioinformatic prediction of themolecular links between Alzheimer’s disease and diabetes mellitus, Peer J 11, e14738.

83.

Alamro

, Bajic

, Macvanin

, Isenovic

, Gojobori

, Essack

, Gao

(2022) Type 2 diabetes mellitus and its comorbidity, Alzheimer’s disease: Identifying critical microRNA using machine learning. Front Endocrinol 13, 1084656.

84.

Profenno

, Porsteinsson

, Faraone

(2010) Meta-analysis of Alzheimer’s disease risk with obesity, diabetes,and related disorders. Biol Psychiatry 67, 505–512.

85.

Pugazhenthi

, Qin

, Reddy

(2017) Common neurodegenerative pathways in obesity, diabetes, and Alzheimer’s disease. Biochim Biophys Acta Mol Basis Dis 1863, 1037–1045.

86.

Zhang

, He

, Su

, Zhang

, Meng

(2020) Association between chronic kidney disease and Alzheimer’sdisease: An update. Metab Brain Dis 35, 883–894.

87.

Monsour

, Dutta

, Mohamed

, Borkowski

, Viswanadhan

(2022) Neuroimaging in the era of artificial intelligence: Current applications, Fed Pract 39, S14–S20.

88.

Vieira

, Pinaya

WHL

, Mechelli

(2017) Using deep learning to investigate the neuroimaging correlates ofpsychiatric and neurological disorders: Methods and applications. Neurosci Biobehav Rev 74, 58–75.

89.

, Ouyang

, Yuan

(2023) Deep learning aided neuroimaging and brain regulation. Sensors 23, 4993.

90.

Wang

, Zhang

, Shen

, Liu

(2019) Multi-task exclusive relationship learning for Alzheimer’s disease progression prediction with longitudinal data. Med Image Anal 53, 111–122.

91.

Liu

, Liu

, Cai

, Pujol

, Kikinis

, Feng

(2014) Early diagnosis of Alzheimer’s disease with deep learning. In 2014 IEEE 11th International Symposium on Biomedical Imaging, April 2014, 1015-1018.

92.

Casanova

, Barnard

, Gaussoin

, Saldana

, Hayden

, Manson

, Wallace

, Rapp

, Resnick

, Espeland

, Chen

(2018) Using high-dimensional machine learning methods to estimate an anatomical risk factor for Alzheimer’s disease across imaging databases. Neuroimage 183, 401–411.

93.

Kumar

, Oh

, Schindler

, Lai

, Payne

PRO

, Gupta

(2021) Machine learning for modeling the progression ofAlzheimer disease dementia using clinical data: A systematic literature review, JAMA Open 4, ooab05.

94.

Dansson

, Stempfle

, Egilsdóttir

, Schliep

, Portelius

, Blennow

, Zetterberg

, Johansson

(2021) Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer’s disease. Alzheimers Res Ther 13, 151.

95.

Rye

, Vik

, Kocinski

, Lundervold

(2022) Predicting conversion to Alzheimer’s disease inindividuals with Mild Cognitive Impairment using clinically transferable features. Sci Rep 12, 15566.

96.

Minhas

, Khanum

, Alvi

, Riaz

, Khan

, Alsolami

, Khan

(2021) Early MCI-to-AD conversion prediction using future value forecasting of multimodal features. Comput Intell Neurosci 2021, 6628036.

97.

Meier

, Buegler

, Harms

, Seixas

, Çöltekin

, Tarnanas

(2021) Using a digital neuro signature to measure longitudinal individual-level change in Alzheimer’s disease: The Altoida large cohort study. NPJ Digit Med 4, 101.

98.

Menze

, Jakab

, Bauer

, Kalpathy-Cramer

, Farahani

, Kirby

, Burren

, Porz

, Slotboom

, Wiest

, Lanczi

, Gerstner

, Weber

, Arbel

, Avants

, Ayache

, Buendia

, Collins

, Cordier

, Corso

, Criminisi

, Das

, Delingette

, Demiralp

, Durst

, Dojat

, Doyle

, Festa

, Forbes

, Geremia

, Glocker

, Golland

, Guo

, Hamamci

, Iftekharuddin

, Jena

, John

, Konukoglu

, Lashkari

, Mariz

, Meier

, Pereira

, Precup

, Price

, Raviv

, Reza

SMS

, Ryan

, Sarikaya

, Schwartz

, Shin

, Shotton

, Silva

, Sousa

, Subbanna

, Szekely

, Taylor

, Thomas

, Tustison

, Unal

, Vasseur

, Wintermark

, Ye

, Zhao

, Zikic

, Prastawa

, Reyes

, Van Leemput

(2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transactions on Medical Imaging 34, 993–2024.

99.

Sharma

, Aggarwal

(2010) Automated medical image segmentation techniques. J Med Phys 35, 3–14.

100.

Wang

, Li

, Wang

, Liu

, Wang

, Tan

, Wu

, Liu

, Sun

, Yang

, Liu

, Chen

, Zhou

, Ayed

, Zheng

(2021) Annotation-efficient deep learning for automatic medical image segmentation. Nat Commun 12, 5915.

101.

Yamanakkanavar

, Choi

, Lee

(2020) MRI segmentation and classification of human brain using deep learning for diagnosis of Alzheimer’s disease: A survey. Sensors 20, 3243.

102.

Gold

, Amatniek

, Carrillo

, Cedarbaum

, Hendrix

, Miller

, Robillard

, Rice

, Soares

, Tome

, Tarnanas

, Vargas

, Bain

, Czaja

(2018) Digital technologies as biomarkers, clinical outcomes assessment, and recruitment tools in Alzheimer’s disease clinical trials. Alzheimers Dement (N Y) 4, 234–242.

103.

Nahavandi

, Alizadehsani

, Khosravi

, Acharya

(2022) Application of artificial intelligence in wearable devices: Opportunities and challenges. Comput Methods Programs Biomed 213, 106541.

104.

Sujith

AVLN

, Sajja

, Mahalakshmi

, Nuhmani

, Prasanalakshmi

(2022) Systematic review of smart health monitoring using deep learning and Artificial intelligence. Neurosci Inform 2, 100028.

105.

Iqbal

SMA

, Mahgoub

, Du

, Leavitt

, Asghar

(2021) Advances in healthcare wearable devices. NPJ Flex Electron 5, 9.

106.

Gutierrez

, Rabbani

, Ajayi

, Gebresilassie

, Rafferty

, Castro

, Banos

(2021) Internet of things formental health: Open issues in data acquisition, self-organization, service level agreement, and identitymanagement. Int J Environ Res Public Health 18, 1327.

107.

Fristed

, Skirrow

, Meszaros

, Lenain

, Meepegama

, Cappa

, Aarsland

, Weston

(2022) A remote speech-based AI system to screen for early Alzheimer’s disease via smartphones, Alzheimers Dement (Amst) 14, e12366.

108.

Lancioni

, Singh

, O’Reilly

, Sigafoos

, D’Amico

, De Vanna

, Laporta

, De Caro

, Pinto

(2020) Smartphone technology for fostering goal-directed ambulation and object use in people with moderate Alzheimer’s disease. Disabil Rehabil Assist Technol 15, 754–761.

109.

Stavropoulos

, Lazarou

, Diaz

, Gove

, Georges

, Manyakov

, Pich

, Hinds

, Tsolaki

, Nikolopoulos

, Kompatsiaris

(2021) Wearable devices for assessingfunction in Alzheimer’s disease: A European public involvementactivity about the features and preferences of patients andcaregivers. Front Aging Neurosci 13, 643135.