Longitudinal study of Alzheimer's disease progression through MRI markers: A Systematic Review

Longitudinal study of Alzheimer's disease progression through MRI markers: A Systematic Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Longitudinal study of Alzheimer's disease progression through MRI markers: A Systematic Review Masoud Lotfalipoura, Maryam Sadat Banifatemeh, Bahareh Amini, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9688545/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This systematic review synthesizes evidence from longitudinal MRI studies to evaluate how cortical thickness and brain atrophy serve as distinct biomarkers for Alzheimer's disease (AD). The goal is to differentiate their specific roles in tracking disease onset versus progression. Methods Following PRISMA guidelines, a systematic search of four major databases was conducted for longitudinal MRI studies of structural brain changes in AD. From an initial 10,908 records, eight studies were selected based on pre-defined eligibility criteria, including the presence of an AD patient group and a healthy control comparison. The quality of the included studies was systematically assessed. Results The findings reveal distinct, stage-dependent roles for MRI biomarkers. Cortical thinning serves as a sensitive early marker, detectable years before clinical symptoms, making it an indicator of disease onset. In contrast, volumetric atrophy and its rate of change are robust markers of disease progression in established AD. The disease follows a predictable pattern of degeneration, beginning with cortical thinning and advancing to widespread atrophy. Conclusion Longitudinal MRI analysis confirms that cortical thinning and volumetric atrophy are distinct biomarkers for different stages of AD. Cortical thinning signals disease onset, while the rate of atrophy tracks its progression. This dynamic approach positions MRI as a valuable non-invasive tool for disease monitoring, though clinical utility depends on improved standardization. Cognitive Neuroscience Nuclear Medicine & Medical Imaging Alzheimer’s disease brain atrophy magnetic resonance imaging structural biomarkers cortical thickness Figures Figure 1 1. Introduction Dementia is a significant global health issue, representing the seventh leading cause of death and a Major cause of disability and dependency in older populations.( 1 ) The most common form of dementia is Alzheimer's disease (AD), which is responsible for 60–70% of cases.( 1 ) The scale of this crisis is particularly evident in the United States, where an estimated 7.2 million Americans aged 65 and older are living with Alzheimer’s dementia in 2025 ( 2 ). The prevalence of AD is expected to increase as the global population ages, placing an ever-growing burden on healthcare systems and society ( 2 ). The neuropathological basis of Alzheimer's disease (AD) is the substantial accumulation of amyloid-β (Aβ) plaques and neurofibrillary tangles (NFTs) in the brain. These pathologies are believed to lead to neuroinflammation, synaptic dysfunction, mitochondrial and bioenergetic disturbances, and vascular abnormalities, ultimately resulting in widespread neuronal death and structural brain changes ( 3 , 4 , 5 ). A significant body of literature has focused on the early biomarkers of Alzheimer's disease (AD). It is well-established that neuropathological changes begin decades before the onset of clinical symptoms ( 6 ), gradually leading to cortical thinning and cerebral atrophy ( 7 , 8 , 9 ). These structural brain changes, however, are not random; they follow a gradual process with a distinct signature ( 10 , 11 ). While numerous studies have utilized MRI to explore these changes in cross-sectional designs, a significant gap remains in research focusing on the longitudinal progression of AD using MRI ( 12 , 13 , 14 ). Longitudinal studies are essential for understanding how brain structures change over time and for identifying MRI markers that are most indicative of disease progression ( 15 , 16 ). Our goal is to critically evaluate the changes in cortical thickness and brain atrophy over time, focusing on their ability to serve as reliable biomarkers for monitoring AD progression. Through this review, we aim to provide a comprehensive synthesis of the current evidence on the longitudinal aspects of AD as observed through MRI, and to contribute to the ongoing debates surrounding the validity and applicability of MRI in clinical practice and research. 2. Methods This systematic review was conducted and is reported following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines ( 17 ). 2.1 Databases and search strategy In February 2024, a literature search was conducted across four scientific databases: PubMed, Scopus, Web of Science, and Google Scholar. The objective of the search was to identify longitudinal MRI studies that investigate structural brain changes in patients with Alzheimer's disease (AD). The following search string was applied to all databases: (Alzheimer OR AD) AND (MRI OR 'Magnetic Resonance Imaging' OR Neuroimaging) AND (longitudinal OR 'Follow Up' OR 'Follow-Up'). Afterwards, all identified records were imported into EndNote, where duplicates were removed. 2.2. Eligibility criteria We established our objectives using the Population, Exposure, Comparison, Outcome and Study Design (PECOS) framework, which is used to formulate clear questions for studies related to health outcomes ( 18 ). Specifically, we included studies in which the Population (P) consisted of patients with a clinical diagnosis of Alzheimer's disease. The Exposure (E) of interest was the use of Magnetic Resonance Imaging (MRI) to track changes over time. We required that studies feature a Comparison (C) group of healthy individuals without Alzheimer's disease and report on Outcomes (O) related to evaluation of structural brain changes during the progression of the disease. Finally, eligible Study Designs (S) included original research articles with a cohort, follow-up, or longitudinal design. The specific exclusion criteria derived from the PECOS framework were as follows: We excluded articles based on their publication type, including non-English papers, review articles (e.g., meta-analyses, systematic reviews), case reports, book chapters, and conference abstracts. Studies were also excluded based on their design, specifically cross-sectional studies, those involving a treatment intervention, and secondary analyses of publicly available datasets, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). Additionally, we excluded studies that utilized neuroimaging approaches other than MRI (e.g., PET, fMRI, EEG). Finally, all animal studies and any research focusing on participants with mild cognitive impairment (MCI) or subjective cognitive decline (SCD) instead of a formal diagnosis of Alzheimer's disease (AD) were excluded. 2.3. Study Selection After removing duplicate records, the remaining articles were imported into the Rayyan QCRI systematic review software, where they were screened by two independent reviewers. The screening process was conducted in two stages. First, titles and abstracts were reviewed, resulting in the selection of 184 potentially relevant articles for full-text assessment. Then, the full texts of these selected articles were retrieved and reviewed for eligibility. This eligibility assessment was conducted blindly by the reviewers. Throughout this process, any disagreements between reviewers were resolved through discussion or consultation with a third expert. Finally, eight articles that met all pre-defined criteria were selected for the final review. 2.4. Data extraction After selecting the final set of papers, a data extraction sheet was designed to extract information in a consistent manner. The extracted data included author name, publication year, study location, study aim, sample characteristics, follow-up duration, and primary outcomes related to measurements of brain structure. 2.5. Risk of bias assessment The methodological quality and risk of bias for each included study were independently assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Cohort Studies ( 19 ). This assessment was performed by two reviewers using the same consensus process described in the Study Selection section, wherein a third author served as an arbiter for any disagreements. Each study was evaluated using the tool's 11 questions, which assess key domains including the study population, measurement of exposure, and control of confounding factors . The complete checklist of questions can be found in the Supplementary Materials (see supplementary Table S1). Following the tool's instructions, the overall risk of bias for each study was then classified as low, high, or unclear. The results of this quality appraisal were considered during the synthesis and interpretation of the final findings. 3. Results The initial search across four databases yielded 10,908 records, which were subsequently reduced to 4,223 unique articles after removing duplicates. A subsequent screening of titles and abstracts led to the selection of 184 articles for full-text assessment. Ultimately, eight studies that met all eligibility criteria were included in the review ( 21 – 28 ). The entire process is illustrated in the PRISMA flow diagram (Fig. 1 ). 3.1. Study characteristics Key characteristics of the studies, including their design, participant demographics, and methodological features, are summarized in Table 1 . The following sections provide a narrative overview of these characteristics 3.1.1 Participant Demographics and Study Details Studies were published between 2009 and 2021 in diverse geographical locations, including Europe, North America, Australia, and South America. Collectively, these studies included 739 participants, with a mean baseline age of 63.45 years and a nearly balanced sex distribution where women comprised approximately 47.5% of the total cohort. 3.1.2. Clinical Measures and Comparison Groups Across all included studies, the Mini-Mental State Examination (MMSE) was the standard tool used to confirm the diagnosis of Alzheimer's disease. To assess a broader range of cognitive functions, the studies employed a variety of other neuropsychological tests targeting various domains. In addition to the comparison with healthy controls, several studies included other clinical groups such as patients with early-onset Alzheimer's disease (EOAD) ( 20 ), mild cognitive impairment (MCI) ( 21 – 22 ), dementia with Lewy bodies (DLB) ( 23 ), frontotemporal dementia (FTD) and semantic dementia (SD) ( 24 ). Table 1 Study population and characteristics Study Country Follow-up Duration Study Design Main Population Primary Outcome Progression measures (Biomarkers of Progression) Adriaanse et al., 2014 The Netherlands & USA 2.5 years longitudinal AD Progression Atrophy & Cortical Volume Loss Contador et al., 2021 Spain & USA 2 years longitudinal EOAD Onset and Progression Cortical Thickness and Gray Matter Volume Henneman et al., 2009 The Netherlands & UK 1.8 years longitudinal AD & MCI Progression Whole Brain/Hippocampal Atrophy Krueger et al., 2010 USA 14 months longitudinal AD & FTD & SD Progression Atrophy/Volume loss Mak et al., 2015 Australia 1 year longitudinal AD & DLB Progression Whole Brain Atrophy Villemagne et al., 2013 Australia 3.8 years longitudinal AD & MCI Progression Gray Matter Atrophy Weiler et al., 2015 Italy & Brazil 16 ± 3 months longitudinal AD Progression Gray Matter Atrophy Weston et al., 2016 UK 2.9 years longitudinal FAD Onset Cortical Thickness 3.2. Baseline Differences This section details the baseline differences between patients with Alzheimer's disease (AD) and healthy controls (HC), presented separately for volumetric and cortical thickness metrics. Of the eight included studies, six provided data on baseline volumetric comparisons, while two reported on cortical thickness. 3.2.1. Volume Loss and Atrophy Baseline volumetric analyses consistently revealed significant brain atrophy in patients with Alzheimer's disease (AD) when compared to healthy controls (HC). A frequently reported finding was a reduction in the volume of hippocampus ( 15 ). In a sample of individuals with early-onset AD (EOAD), Contador and his colleagues confirmed lower volumes in bilateral hippocampus and amygdala, which were accompanied by a corresponding increase in the volume of inferior-lateral ventricles ( 25 ). A broader, distributed pattern of cortical atrophy was also described. Weiler and his team reported that at baseline, AD patients exhibited a disrupted pattern of cortical atrophy, with significant involvement of medial temporal lobe, inferior parietal and postcentral gyri, and posterior cingulate, bilaterally ( 26 ). Supporting this pattern of widespread atrophy, Adriaanse and his colleagues observed lower cortical volume in AD patients across a combined set of 'AD-signature regions' that included inferior frontal cortex, posterior cingulate cortex, temporal-polar cortex, lateral temporal cortex, inferior parietal sulcus, inferior parietal cortex, and medial temporal lobe ( 27 ). In contrast, Kruger reported that baseline volumes were not significantly different between diagnostic groups in parietal and occipital lobes ( 24 ). Their analysis focused on temporal lobe differences for differential diagnosis, noting that patients with semantic dementia (SD) had significantly different right temporal lobe volumes compared to controls, the volumetric difference was also significant when compared against both controls and patients with Alzheimer's disease (AD) in left temporal lobe ( 24 ). 3.2.2 Cortical Thickness Baseline differences in cortical thickness were a key finding in two studies that focused on early and genetic forms of Alzheimer's disease. In a study conducted by Contador and his colleagues, involving patients with early-onset Alzheimer's disease (EOAD), reduced cortical thickness was observed in both hemispheres compared to healthy controls. Following a vertex-wise analysis, a distinct pattern of thinning was highlighted, particularly in posterior regions, such as precuneus and the temporoparietal junction ( 25 ). Similarly, Weston studied a sample with familial AD (FAD) and identified significantly reduced mean cortical thickness within a predefined 'cortical signature' comprising six regions: entorhinal cortex, inferior parietal cortex, precuneus, superior frontal cortex, superior parietal cortex, and supramarginal gyrus. These findings established that the differences were detectable up to three years before the predicted onset of symptoms, highlighting cortical thinning as a key pre symptomatic feature ( 28 ). 3.3. Longitudinal Changes 3.3.1. Volume Loss and Atrophy Longitudinal analyses revealed that patients with Alzheimer's disease (AD) exhibit a significantly faster rate of brain atrophy compared to healthy controls (HC). Adriaanse reported that AD patients lose approximately 3.5% of cortical volume over 2.5 years, compared to just 0.6% in controls ( 27 ). Similarly, Mak found that the annual rate of brain volume loss in AD patients is approximately − 1.8%, which is double the rate observed in controls ( 23 ). The topographical progression of this gray matter atrophy follows a distinct anatomical pattern. This process involves accelerated atrophy in multiple cortical lobes, including temporal, occipital, and parietal lobes, as well as periventricular regions ( 23 ), and key frontal areas such as inferior frontal and orbitofrontal cortices ( 26 ). The atrophy also extends into subcortical structures, including thalamus, cerebellum, putamen, and caudate, with the most significant volume reductions observed in bilateral hippocampus, amygdala, and thalamus ( 25 ). Furthermore, a more aggressive rate of atrophy has been associated with an earlier age of disease onset ( 23 ). The specific topographical pattern of atrophy differs among dementia groups. Analyses by Krueger and his colleagues revealed that atrophy rates in frontal lobes of FTD patients were twice as high as those seen in AD patients; likewise, atrophy rates in temporal lobes of SD patients were double those of AD patients ( 24 ). While the topographical pattern of atrophy distinguished the groups, the overall rate of atrophy in AD was not significantly faster than other dementia groups ( 24 ). The unique sensitivity of the hippocampus was highlighted by Henneman, such that among MCI patients, higher hippocampal atrophy rates were a strong predictor of progression to a clinical AD diagnosis ( 21 ). However, the same study noted that whole-brain atrophy rates were better at discriminating between patients with established AD and those with MCI, illustrating that the optimal biomarker may differ depending on the disease stage ( 21 ). 3.3.2. Cortical Thickness The studies identified distinct, yet overlapping, topographical patterns of change. For instance, Weston defined a 'cortical signature' of six regions that showed significant thinning in familial AD (FAD) ( 28 ). The study then demonstrated that by monitoring the rate of change, a significantly faster rate of thinning could be detected in precuneus as early as eight years before the predicted onset of symptoms ( 28 ). Complementing this, Contador found a posterior-dominant pattern of thinning in early-onset AD (EOAD), with most significant changes occurring in bilateral entorhinal, parahippocampal, and precuneus regions ( 25 ). Detection of these changes in pre symptomatic stages was a key finding. Weston established that differences in cortical thickness were detectable as early as four years before the predicted onset of symptoms, first appearing in precuneus. Furthermore, they demonstrated that a lower baseline cortical thickness predicted a greater rate of subsequent thinning in FAD mutation carriers ( 28 ). The importance of the rate of change was highlighted by Contador who found that regions with greater baseline thinning experienced more atrophy over a two-year follow-up period ( 25 ). 3.4. Progression and Onset Predictors Four of the included studies identified specific MRI measures that could predict progression or onset of Alzheimer’s disease (AD). The rate of atrophy was highlighted as a key dynamic marker. Krueger stated that because the rate of regional atrophy correlates closely with the speed of clinical changes, it can serve as a valuable tool for monitoring disease progression ( 24 ). Similarly, Henneman found that both a higher rate of hippocampal atrophy and a lower baseline hippocampal volume were strong, independent predictors of progression to AD in patients without a diagnosis of dementia at baseline ( 21 ). For patients already diagnosed with mild cognitive impairment (MCI), this study identified a low baseline hippocampal volume as the strongest predictor of future conversion to AD. Cortical thickness, on the other hand, characterizes the prodromal stage of the disease. Weston found that the earliest significant cortical thickness difference between groups appeared in the precuneus region, detectable up to four years before disease onset ( 28 ). This early precuneus thinning may therefore represent a prodromal marker of Alzheimer’s disease onset, rather than an indicator of its progression. Table 2 Alzheimer’s Disease Progression study methodologies and main results. Study Sample size Age, years (mean ± SD) Female n (%) Region Measures/tools Main results Adriaanse et al., 2014 21 62.82 (6.23) 3 (14.29) Inferior-Frontal Cortex Posterior Cingulate Cortex Temporal Polar Cortex Lateral Temporal Cortex Inferior-Parietal Sulcus Medial Temporal Lobe T-MRI scans MMSE Clinical and neuropsychological evaluations CSF biomarker samples APOE genotype Connection between baseline [18F] FDG uptake in the precuneus and subsequent volume loss in Alzheimer's disease (AD) patients; lower metabolism at baseline is associated with greater cortical volume reduction over time. This relationship was absent in normal controls. Baseline [11C] PIB binding did not correlate with cortical volume loss over time in either the AD group or the control group. Contador et al., 2021 100 57.25 (4.86) 56 (56.00) Bilateral Entorhinal Right Fusiform Left Precuneus Bilateral Parahippocampal Regions Bilateral Hippocampus Amygdala Thalamus Cerebellum Gray Matter Left Caudate Putamen Regions MRI Neurologic and neuropsychological assessment MMSE Clinical Dementia Rating scale Wechsler Abbreviated Scale of Intelligence Recognition Memory Test Forward and backward digit span Graded Naming Test Graded Difficulty Arithmetic Test Visual Object and Space Perception battery Longitudinal analysis over two years indicated that progressive atrophy extended throughout the neocortex, exhibiting a posterior-to-anterior gradient and affecting areas beyond the initial subcortical structures. In early-onset Alzheimer's disease (EOAD), cortical thinning was more pronounced in posterior regions compared to healthy controls. Additionally, initial cerebrospinal fluid (CSF) A-Beta42 levels near the normality threshold were linked to accelerated cortical loss in these posterior areas in EOAD. Elevated baseline t-tau levels were associated with increased rates of volume loss in the medial temporal lobe subcortical structures among EOAD patients. Henneman et al., 2009 142 67 ( 9 ) 75 (52.82) Hippocampus Whole Brain Clinical and neuropsychological evaluations MRI assessments Cambridge Cognitive Examination (CAMCOG) MMSE Unified Parkinson3s Disease Rating Scale Part III Neuropsychiatric Inventory Cognitive fluctuation scale In individuals without dementia, regional hippocampal measures were the most significant indicators of progression to Alzheimer's disease (AD). Additionally, in this group, the rate of whole brain atrophy offered an independent predictive value, enhancing the prediction beyond what hippocampal measures alone could achieve. In patients with mild cognitive impairment (MCI), initial hippocampal atrophy emerged as the most robust predictor of progression to AD. Krueger et al., 2010 68 60 (8.3) 27 (39.71) Parietal Lobe Clinical and neuropsychological assessments MRI (VBM/ TBM/ DT MRI) CSF sample by lumbar puncture FDG PET SPECT MMSE Prose memory test Verbal and spatial span Rey’s Figure Delayed Recall Test Rey’s Figure Copy Test Raven’s Coloured Progressive test Attentive matrices Phonemic and Semantic Fluency tests Token Test The rate of volume loss is specific to each disease and is more pronounced in regions that have the smallest baseline volume. In the AD group, the highest rate of atrophy change was observed in the parietal lobes. Additionally, the atrophy rates in groups with SD and FTD were twice as high as those observed in the AD group. Mak et al., 2015 72 76.8 (5.5) 24 (33.33) Periventricular areas Record of medical history Neuropsychological testing (MMSE etc.) Physical and neurological examination Screening laboratory tests MRI PET Over a one-year period, the Alzheimer's disease (AD) group exhibited a significantly higher rate of atrophy compared to the dementia with Lewy bodies (DLB) group. The DLB group demonstrated an atrophy rate similar to that of healthy controls (HC). Among subjects with dementia, a younger age at baseline was associated with a more aggressive rate of atrophy. The AD group showed an increased whole brain atrophy rate in comparison to HC. Villemagne et al., 2013 200 69.8 (9.4) 96 (48.00) Hippocampus ¹¹C-PiB PET MRI scan Neuropsychological examination Rey complex figure test California verbal learning test second edition (CVLT-II) Boston naming test APOE Genotype Clinical dementia rating MMSE The rate of Aβ deposition appears to decrease at higher Aβ burdens and lower MMSE scores, indicating that Aβ deposition slows during the later stages of AD. Additionally, a higher baseline level of Aβ is predictive of the subsequent rate of brain atrophy over time. Hippocampal atrophy is projected to occur significantly earlier than the clinical symptoms of dementia, specifically approximately 4.2 years prior to the onset of dementia. Weiler et al., 2015 51 65.4 (6.2) 24 (47.06) Medial Temporal Lobe Inferior Parietal Lobe Postcentral Gyri Bilateral Posterior Cingulum Hippocampus Lateral Temporal Cortex Temporal Pole Inferior Frontal & Orbitofrontal Cortices Inferior & Superior Parietal Lobe Posterior Cingulate Cortex Cerebellum Bilateral Putamen Left Inferior Cingulate Cortex Insula Right Caudate Nucleus MRI MMSE At baseline, patients with Alzheimer's disease (AD) exhibited gray matter atrophy and white matter microstructural damage in regions including the medial temporal, posterior cingulate, and inferior parietal cortices, as well as the corpus callosum and cingulum. Over the follow-up period, both gray matter atrophy and alterations in white matter microstructure continued to progress. Notably, the accumulation of white matter damage was not associated with baseline gray matter or with gray matter tissue loss over time. Furthermore, AD patients with higher levels of cerebrospinal fluid (CSF) t-tau at baseline demonstrated a greater progression of white matter damage Weston et al., 2016 85 48.5 (8.8) 52 (61.18) Entorhinal Cortex Inferior Parietal Cortex Precuneus Superior Frontal Cortex Superior Parietal Cortex Supramarginal Gyrus MRI MMSE Medical history Neuropsychological and neurologic examination Petersen criteria for the MCI Visual association test Category fluency test Instrumental Activities of Daily Living Scale Cortical thickness was considerably lower in six key regions including the entorhinal cortex, the inferior parietal cortex, the precuneus, the superior frontal cortex, the superior parietal cortex, and the supramarginal gyrus in mutation carriers 3 years before the onset of cognitive symptoms. The precuneus is among the first regions to exhibit a significant reduction in cortical thickness, occurring approximately four years prior to the onset of symptoms. 4. Discussion This systematic review examines longitudinal MRI studies to provide a new spatio-temporal perspective on grey matter degeneration in Alzheimer's disease. To the best of our knowledge, this is the first study to differentiate between cortical thickness and grey matter atrophy as separate biomarkers, each serving a distinct predictive purpose. We observed a progressive degeneration across distinct anatomical and clinical stages, from presymptomatic cortical thinning to widespread cortico-subcortical atrophy. Given the limitations of static imaging techniques ( 29 – 30 ), we emphasize the importance of repeated biomarker assessments over time ( 31 – 34 ). We refer to this approach as a dynamic spatio-temporal biomarker and propose its usefulness for early detection and management of Alzheimer's disease progression. A key finding of this review with significant clinical implications is the distinct utility of cortical thickness versus volumetric atrophy as stage-dependent biomarkers. The data show that cortical thinning, particularly its rate of change, serves as a sensitive prodromal marker, detectable years before the predicted onset of symptoms. In contrast, volumetric atrophy has a unique spatio-temporal signature in established AD and is more indicative of disease progression, with whole-brain atrophy rates effectively discriminating between MCI and AD stages. A thorough understanding of this distinction, where cortical thinning signals the onset and atrophy rates track the progression, is a productive step toward improving disease management and designing stage-appropriate clinical trials. The earliest structural changes identified in longitudinal studies occur during the presymptomatic phase. Accelerated cortical thinning, particularly in posterior brain regions such as the precuneus, posterior cingulate, and entorhinal cortex, emerges several years before cognitive symptoms develop ( 25 , 28 ). Notably, Weston demonstrated that cortical thinning of precuneus in familial AD could be detected up to eight years prior to predicted symptom onset ( 28 ). These findings align with amyloid accumulation timelines ( 22 ) and support the conceptualization of AD as a protracted continuum, with subtle structural degeneration preceding clinical manifestation ( 35 ). However, while cortical thinning is a sensitive early marker, the utility of structural MRI for detecting preclinical AD in routine practice remains limited, mainly due to overlap with normative aging and the subtlety of changes at this stage ( 36 – 38 ). As the disease advances to the stage of mild cognitive impairment (MCI), hippocampal atrophy becomes more prominent. While static hippocampal volume has demonstrated moderate sensitivity as a diagnostic feature ( 39 ), longitudinal studies reveal that the rate of hippocampal atrophy is a stronger predictor of clinical progression ( 21 ). This dynamic measure consistently outperforms whole-brain atrophy in predicting the transition from MCI to AD ( 40 ), underscoring the importance of tracking atrophy rates rather than relying solely on baseline measurements. This review confirms that hippocampal degeneration represents a critical pathological milestone, coinciding with the onset of clinically detectable memory impairment. Standardized segmentation and consistent imaging protocols remain essential to improve cross-study comparability ( 41 – 42 ). In established AD, neurodegeneration extends beyond medial temporal lobes, involving a broader cortical and subcortical network. The atrophy pattern typically spreads from temporal lobes to parietal and occipital regions, contributing to visuospatial and broader cognitive deficits ( 23 ). Degeneration in deep gray matter structures—including amygdala, thalamus, caudate, and putamen—further emphasizes that AD is a whole-brain disorder, rather than limited to cortical regions ( 25 ). The distinct topographical pattern of atrophy holds a significant value for differential diagnosis. Studies show that the highest rate of atrophy in AD occurs in parietal lobes. In contrast, Frontotemporal Dementia (FTD) primarily involves atrophy of frontal lobe, while Semantic Dementia (SD) has a temporal lobe focus (Krueger et al., 2010). Furthermore, compared to dementia with Lewy bodies (DLB), AD is marked by accelerated global volume loss, particularly in periventricular areas ( 23 ), confirming MRI's role in distinguishing between dementia subtypes. 5. Conclusion Longitudinal structural MRI studies consistently demonstrate that Alzheimer’s disease (AD) progresses in a spatiotemporal pattern: beginning with subtle cortical thinning in posterior brain regions during the presymptomatic phase, followed by hippocampal atrophy during progression to mild cognitive impairment (MCI), and culminating in widespread cortico-subcortical and white matter degeneration in established dementia. Among structural biomarkers, hippocampal atrophy rates and cortical thinning trajectories show the strongest predictive value for clinical progression, outperforming static measures. Overall, MRI-based structural biomarkers offer a non-invasive window into the temporal evolution of AD pathology, supporting their integration into early detection frameworks, individualized prognostic models, and ongoing disease monitoring. However, their clinical utility depends on improved standardization, multimodal integration, and further validation across diverse populations. 6. Limitations This systematic review provides important insights into Alzheimer’s disease (AD) progression using longitudinal MRI markers, yet several limitations should be acknowledged. The review primarily focuses on cortical thickness and brain atrophy, excluding other potentially valuable MRI features such as white matter integrity and functional changes, which could have expanded the findings. Additionally, it focuses solely on structural MRI, leaving out multimodal imaging modalities like PET that might provide a more comprehensive understanding of AD pathology. The inclusion criteria and search strategy may have inadvertently excluded relevant studies, particularly non-English publications, potentially introducing selection bias. The reliance on published data further amplifies the risk of publication bias, as studies with null findings may be underrepresented. The variability in methodologies, imaging protocols, and outcome measures across the included studies posed challenges for synthesis and may have affected the robustness of conclusions. Finally, while subgroup analyses such as early-onset versus typical AD were considered, demographic factors like age, sex, and genetic predispositions were not extensively explored, limiting the nuance of the findings. Declarations Acknowledgements During the preparation of this manuscript, the authors utilized Google's Gemini generative AI to enhance grammar, phrasing, and overall readability. All suggestions were critically reviewed by the authors, who retain full responsibility for the accuracy and integrity of the work presented. Authors contribution M.L. and M.Sa. wrote the manuscript. M.L. and F.H. performed data extraction and Risk of Bias assessment. M.L. designed the tables. M.L., M.B., B.A., F.H., P.M., and M.So. screened the paper pool. A.Z. supervised the entire project. Disclosure statement The authors report there are no competing interests to declare Biographical note Mohammad saemi: After an insightful bachelor’s experience in psychology, I became unsatisfied with the lack of an objective perspective on the human mind within the field. At the same time, I grew increasingly fascinated by the nature of academic research, which led me to pursue a career in cognitive science. My goal is to gain a deeper understanding of the human brain through advanced neuroimaging and neuromodulatory technologies. My main research interest focuses on time perception and its relationship with various mental disorders, approached through a dynamic complex network perspective on the brain and explored with more nuanced methods of effective connectivity. As a lecturer, I have taught the fundamentals of image processing using the Statistical Parametric Mapping (SPM) package. I also consider myself a transhumanist, aspiring to advance our cognitive capacities through what the literature describes as cognitive enrichment. Masoud Lotfalipour: Masoud Lotfalipour is a master’s student in Cognitive Science at the Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran. His research focuses on neuroimaging and brain connectivity, with particular interest in neurodegenerative disorders such as Alzheimer’s disease and Parkinson’s disease. He has experience working with multimodal datasets, including structural MRI, fMRI, and DTI, and applies advanced computational and statistical methods to investigate brain changes associated with aging and disease progression. Maryam Sadat Banifatemeh: Maryam Sadat Banifatemeh holds an M.A. in Clinical Psychology from Allameh Tabataba’i University, Tehran, Iran. Her research interests include neuropsychology, executive brain functions, and the cognitive and neural mechanisms underlying psychiatric disorders, with applications in evidence-based psychotherapeutic interventions. She is currently conducting various applied, quantitative, and longitudinal clinical studies examining the relationships between brain function, cognition, and mental health. Fatemeh Hemmat: Fatemeh Hemmat is a graduate of Russian linguistics from faculty of foreign languages and literature, University of Tehran, Tehran, Iran. She is interested in how mind and brain function to produce and comprehend language in healthy and impaired populations. Moslem Solhirad: With some efforts and large passion to relationship between Science and Philosophy, Moslem eagers to follow implications in Science for better understanding of Experimental Issues in Philosophy. Bahareh Amini: Bahareh holds an M.A. in Genetics and has recently completed her B.A. in Psychology. Her research interests include neuropsychology and clinical psychology, and she is about to begin her M.A. in Clinical Psychology. She is eager to deepen her understanding of the brain and its impact on human behavior and everyday life. Data availability statement The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials. Additional information Funding The authors received no financial support for the research, authorship, and publication of this article. References Greenblat C (2025) Fact sheet: Dementia [Internet] Geneva: World Health Organization; [Available from: https://www.who.int/en/news-room/fact-sheets/detail/dementia (2025) Alzheimer's disease facts and figures. Alzheimer's & Dementia. 2025;21(4):e70235 Abdelnour C, Agosta F, Bozzali M, Fougère B, Iwata A, Nilforooshan R et al (2022) Perspectives and challenges in patient stratification in Alzheimer's disease. 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J Alzheimers Dis 59(4):1359–1379 McGhee DJ, Ritchie CW, Thompson PA, Wright DE, Zajicek JP, Counsell CE (2014) A systematic review of biomarkers for disease progression in Alzheimer's disease. PLoS ONE 9(2):e88854 Fennema-Notestine C, Hagler DJ Jr., McEvoy LK, Fleisher AS, Wu EH, Karow DS et al (2009) Structural MRI biomarkers for preclinical and mild Alzheimer's disease. Hum Brain Mapp 30(10):3238–3253 Mills KL, Tamnes CK (2014) Methods and considerations for longitudinal structural brain imaging analysis across development. Dev Cogn Neurosci 9:172–190 Morgan RL, Whaley P, Thayer KA, Schünemann HJ (2018) Identifying the PECO: A framework for formulating good questions to explore the association of environmental and other exposures with health outcomes. Environ Int 121(Pt 1):1027–1031 Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev 10(1):89 Barker TH, Habibi N, Aromataris E, Stone JC, Leonardi-Bee J, Sears K et al (2024) The revised JBI critical appraisal tool for the assessment of risk of bias for quasi-experimental studies. JBI Evid Synth 22(3):378–388 Contador J, Pérez-Millán A, Tort-Merino A, Balasa M, Falgàs N, Olives J et al (2021) Longitudinal brain atrophy and CSF biomarkers in early-onset Alzheimer's disease. Neuroimage Clin 32:102804 Villemagne VL, Burnham S, Bourgeat P, Brown B, Ellis KA, Salvado O et al (2013) Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: a prospective cohort study. Lancet Neurol 12(4):357–367 Henneman WJ, Sluimer JD, Barnes J, van der Flier WM, Sluimer IC, Fox NC et al (2009) Hippocampal atrophy rates in Alzheimer disease: added value over whole brain volume measures. Neurology 72(11):999–1007 Mak E, Su L, Williams GB, Watson R, Firbank M, Blamire AM et al (2015) Longitudinal assessment of global and regional atrophy rates in Alzheimer's disease and dementia with Lewy bodies. Neuroimage Clin 7:456–462 Krueger CE, Dean DL, Rosen HJ, Halabi C, Weiner M, Miller BL et al (2010) Longitudinal rates of lobar atrophy in frontotemporal dementia, semantic dementia, and Alzheimer's disease. Alzheimer Dis Assoc Disord 24(1):43–48 Contador J, Pérez-Millán A, Tort-Merino A, Balasa M, Falgàs N, Olives J et al (2021) Longitudinal brain atrophy and CSF biomarkers in early-onset Alzheimer's disease. Neuroimage Clin 32:102804 Adriaanse SM, van Dijk KR, Ossenkoppele R, Reuter M, Tolboom N, Zwan MD et al (2014) The effect of amyloid pathology and glucose metabolism on cortical volume loss over time in Alzheimer's disease. Eur J Nucl Med Mol Imaging 41(6):1190–1198 Weiler M, Agosta F, Canu E, Copetti M, Magnani G, Marcone A et al (2015) Following the Spreading of Brain Structural Changes in Alzheimer's Disease: A Longitudinal, Multimodal MRI Study. J Alzheimers Dis 47(4):995–1007 Weston PS, Nicholas JM, Lehmann M, Ryan NS, Liang Y, Macpherson K et al (2016) Presymptomatic cortical thinning in familial Alzheimer disease: A longitudinal MRI study. Neurology 87(19):2050–2057 Lawrence E, Vegvari C, Ower A, Hadjichrysanthou C, De Wolf F, Anderson RM (2017) A Systematic Review of Longitudinal Studies Which Measure Alzheimer's Disease Biomarkers. J Alzheimers Dis 59(4):1359–1379 van Oostveen WM, de Lange ECM (2021) Imaging Techniques in Alzheimer's Disease: A Review of Applications in Early Diagnosis and Longitudinal Monitoring. Int J Mol Sci. ;22(4) Bayram E, Caldwell JZK, Banks SJ (2018) Current understanding of magnetic resonance imaging biomarkers and memory in Alzheimer's disease. Alzheimers Dement (N Y) 4:395–413 Bercu A, Dufouil C, Debette S, Joliot M, Tsuchida A, Helmer C et al (2024) Prediction of dementia risk from multimodal repeated measures: The added value of brain MRI biomarkers. Alzheimers Dement (Amst) 16(2):e12578 Dubois B, von Arnim CAF, Burnie N, Bozeat S, Cummings J (2023) Biomarkers in Alzheimer's disease: role in early and differential diagnosis and recognition of atypical variants. Alzheimers Res Ther 15(1):175 Georgakas JE, Howe MD, Thompson LI, Riera NM, Riddle MC (2023) Biomarkers of Alzheimer’s disease: Past, present and future clinical use. Biomarkers Neuropsychiatry 8:100063 Porsteinsson AP, Isaacson RS, Knox S, Sabbagh MN, Rubino I (2021) Diagnosis of Early Alzheimer's Disease: Clinical Practice in 2021. J Prev Alzheimers Dis 8(3):371–386 Fjell AM, McEvoy L, Holland D, Dale AM, Walhovd KB (2014) What is normal in normal aging? Effects of aging, amyloid and Alzheimer's disease on the cerebral cortex and the hippocampus. Prog Neurobiol 117:20–40 Toepper M (2017) Dissociating Normal Aging from Alzheimer's Disease: A View from Cognitive Neuroscience. J Alzheimers Dis 57(2):331–352 Arrondo P, Elía-Zudaire Ó, Martí-Andrés G, Fernández-Seara MA, Riverol M (2022) Grey matter changes on brain MRI in subjective cognitive decline: a systematic review. Alzheimers Res Ther 14(1):98 Lombardi G, Crescioli G, Cavedo E, Lucenteforte E, Casazza G, Bellatorre AG et al (2020) Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment. Cochrane Database Syst Rev 3(3):Cd009628 Hu X, Meier M, Pruessner J (2023) Challenges and opportunities of diagnostic markers of Alzheimer's disease based on structural magnetic resonance imaging. Brain Behav 13(3):e2925 de Leon MJ, Mosconi L, Blennow K, DeSanti S, Zinkowski R, Mehta PD et al (2007) Imaging and CSF studies in the preclinical diagnosis of Alzheimer's disease. Ann N Y Acad Sci 1097:114–145 van Oostveen WM, de Lange ECM (2021) Imaging Techniques in Alzheimer's Disease: A Review of Applications in Early Diagnosis and Longitudinal Monitoring. Int J Mol Sci. ;22(4) Footnotes This is an average value. Additional Declarations The authors declare no competing interests. Supplementary Files supplementarytable.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9688545","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":638878261,"identity":"f215402f-fbb4-4a18-994a-48a56d7775a2","order_by":0,"name":"Masoud Lotfalipoura","email":"","orcid":"https://orcid.org/0009-0007-5945-9552","institution":"Institute for Cognitive and Brain Science, Shahid Beheshti University, Tehran, Iran","correspondingAuthor":false,"prefix":"","firstName":"Masoud","middleName":"","lastName":"Lotfalipoura","suffix":""},{"id":638878301,"identity":"15aaa7bb-8e51-43b9-96f4-bfe89bce5828","order_by":1,"name":"Maryam Sadat Banifatemeh","email":"","orcid":"https://orcid.org/0009-0005-5432-016X","institution":"Department of Clinical Psychology, Allameh Tabataba'i University, Tehran, Iran","correspondingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"Sadat","lastName":"Banifatemeh","suffix":""},{"id":638878302,"identity":"ae69064e-a13a-41c3-925e-8ec70b653af1","order_by":2,"name":"Bahareh Amini","email":"","orcid":"","institution":"M.Sc in Genetics, Islamic Azad University OF Tehran Varamin Pishva Branch, Iran","correspondingAuthor":false,"prefix":"","firstName":"Bahareh","middleName":"","lastName":"Amini","suffix":""},{"id":638878303,"identity":"a9bc490e-6470-4971-973f-f6eab0e7bedb","order_by":3,"name":"Fateme Hemmat","email":"","orcid":"","institution":"MA in Russian Language, University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Fateme","middleName":"","lastName":"Hemmat","suffix":""},{"id":638878304,"identity":"fa40e323-e859-4f96-bb40-79e47e87cc51","order_by":4,"name":"Parisa Mirzakhani","email":"","orcid":"https://orcid.org/0000-0003-2963-7059","institution":"M.Sc. in School Counseling, Ardakan University , Yazd, Iran","correspondingAuthor":false,"prefix":"","firstName":"Parisa","middleName":"","lastName":"Mirzakhani","suffix":""},{"id":638878305,"identity":"c03f1b1a-5b3c-426d-bca6-99f75ccd0c95","order_by":5,"name":"Mohammad Saemi","email":"","orcid":"https://orcid.org/0009-0007-3677-1439","institution":"Institute for Cognitive and Brain Science, Shahid Beheshti University, Tehran, Iran","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Saemi","suffix":""},{"id":638878306,"identity":"5e6749ed-a3a0-481a-81ab-99c1b7e437cb","order_by":6,"name":"Moslem Solhirad","email":"","orcid":"","institution":"Department of 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07:55:07","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9688545/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9688545/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109296092,"identity":"2ad51469-5d76-47c0-99f1-04807ed59ad6","added_by":"auto","created_at":"2026-05-15 08:45:27","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76199,"visible":true,"origin":"","legend":"\u003cp\u003ePreferred Reporting Items for Systematic reviews and Meta-Analyses\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9688545/v1/d05a24cca9e505af117c755f.jpg"},{"id":109297290,"identity":"3fbfe614-fad8-4d92-bf2e-f89ca1603899","added_by":"auto","created_at":"2026-05-15 08:55:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":389804,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9688545/v1/ece7f4eb-916b-4333-aa48-fc62ac21ed59.pdf"},{"id":109277783,"identity":"3ff3ef4a-7c8a-4b22-aef9-471090ddc3ca","added_by":"auto","created_at":"2026-05-14 15:27:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20729,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable.docx","url":"https://assets-eu.researchsquare.com/files/rs-9688545/v1/0d3f6327486f7e32d7809497.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eLongitudinal study of Alzheimer's disease progression through MRI markers: A Systematic Review\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDementia is a significant global health issue, representing the seventh leading cause of death and a Major cause of disability and dependency in older populations.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) The most common form of dementia is Alzheimer's disease (AD), which is responsible for 60\u0026ndash;70% of cases.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) The scale of this crisis is particularly evident in the United States, where an estimated 7.2\u0026nbsp;million Americans aged 65 and older are living with Alzheimer\u0026rsquo;s dementia in 2025 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The prevalence of AD is expected to increase as the global population ages, placing an ever-growing burden on healthcare systems and society (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe neuropathological basis of Alzheimer's disease (AD) is the substantial accumulation of amyloid-β (Aβ) plaques and neurofibrillary tangles (NFTs) in the brain. These pathologies are believed to lead to neuroinflammation, synaptic dysfunction, mitochondrial and bioenergetic disturbances, and vascular abnormalities, ultimately resulting in widespread neuronal death and structural brain changes (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA significant body of literature has focused on the early biomarkers of Alzheimer's disease (AD). It is well-established that neuropathological changes begin decades before the onset of clinical symptoms (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), gradually leading to cortical thinning and cerebral atrophy (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). These structural brain changes, however, are not random; they follow a gradual process with a distinct signature (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). While numerous studies have utilized MRI to explore these changes in cross-sectional designs, a significant gap remains in research focusing on the longitudinal progression of AD using MRI (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Longitudinal studies are essential for understanding how brain structures change over time and for identifying MRI markers that are most indicative of disease progression (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur goal is to critically evaluate the changes in cortical thickness and brain atrophy over time, focusing on their ability to serve as reliable biomarkers for monitoring AD progression. Through this review, we aim to provide a comprehensive synthesis of the current evidence on the longitudinal aspects of AD as observed through MRI, and to contribute to the ongoing debates surrounding the validity and applicability of MRI in clinical practice and research.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis systematic review was conducted and is reported following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Databases and search strategy\u003c/h2\u003e \u003cp\u003eIn February 2024, a literature search was conducted across four scientific databases: PubMed, Scopus, Web of Science, and Google Scholar. The objective of the search was to identify longitudinal MRI studies that investigate structural brain changes in patients with Alzheimer's disease (AD). The following search string was applied to all databases: (Alzheimer OR AD) AND (MRI OR 'Magnetic Resonance Imaging' OR Neuroimaging) AND (longitudinal OR 'Follow Up' OR 'Follow-Up'). Afterwards, all identified records were imported into EndNote, where duplicates were removed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Eligibility criteria\u003c/h2\u003e \u003cp\u003eWe established our objectives using the Population, Exposure, Comparison, Outcome and Study Design (PECOS) framework, which is used to formulate clear questions for studies related to health outcomes (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Specifically, we included studies in which the \u003cb\u003ePopulation (P)\u003c/b\u003e consisted of patients with a clinical diagnosis of Alzheimer's disease. The \u003cb\u003eExposure (E)\u003c/b\u003e of interest was the use of Magnetic Resonance Imaging (MRI) to track changes over time. We required that studies feature a \u003cb\u003eComparison (C)\u003c/b\u003e group of healthy individuals without Alzheimer's disease and report on \u003cb\u003eOutcomes (O)\u003c/b\u003e related to evaluation of structural brain changes during the progression of the disease. Finally, eligible \u003cb\u003eStudy Designs (S)\u003c/b\u003e included original research articles with a cohort, follow-up, or longitudinal design.\u003c/p\u003e \u003cp\u003eThe specific exclusion criteria derived from the PECOS framework were as follows: We excluded articles based on their publication type, including non-English papers, review articles (e.g., meta-analyses, systematic reviews), case reports, book chapters, and conference abstracts. Studies were also excluded based on their design, specifically cross-sectional studies, those involving a treatment intervention, and secondary analyses of publicly available datasets, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). Additionally, we excluded studies that utilized neuroimaging approaches other than MRI (e.g., PET, fMRI, EEG). Finally, all animal studies and any research focusing on participants with mild cognitive impairment (MCI) or subjective cognitive decline (SCD) instead of a formal diagnosis of Alzheimer's disease (AD) were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Study Selection\u003c/h2\u003e \u003cp\u003eAfter removing duplicate records, the remaining articles were imported into the Rayyan QCRI systematic review software, where they were screened by two independent reviewers. The screening process was conducted in two stages. First, titles and abstracts were reviewed, resulting in the selection of 184 potentially relevant articles for full-text assessment. Then, the full texts of these selected articles were retrieved and reviewed for eligibility. This eligibility assessment was conducted blindly by the reviewers. Throughout this process, any disagreements between reviewers were resolved through discussion or consultation with a third expert. Finally, eight articles that met all pre-defined criteria were selected for the final review.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Data extraction\u003c/h2\u003e \u003cp\u003eAfter selecting the final set of papers, a data extraction sheet was designed to extract information in a consistent manner. The extracted data included author name, publication year, study location, study aim, sample characteristics, follow-up duration, and primary outcomes related to measurements of brain structure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Risk of bias assessment\u003c/h2\u003e \u003cp\u003eThe methodological quality and risk of bias for each included study were independently assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Cohort Studies (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This assessment was performed by two reviewers using the same consensus process described in the Study Selection section, wherein a third author served as an arbiter for any disagreements. Each study was evaluated using the tool's 11 questions, which assess key domains including the study population, measurement of exposure, and control of confounding \u003cb\u003efactors\u003c/b\u003e. The complete checklist of questions can be found in the Supplementary Materials (see supplementary Table S1). Following the tool's instructions, the overall risk of bias for each study was then classified as low, high, or unclear. The results of this quality appraisal were considered during the synthesis and interpretation of the final findings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe initial search across four databases yielded 10,908 records, which were subsequently reduced to 4,223 unique articles after removing duplicates. A subsequent screening of titles and abstracts led to the selection of 184 articles for full-text assessment. Ultimately, eight studies that met all eligibility criteria were included in the review (\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26 CR27\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The entire process is illustrated in the PRISMA flow diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Study characteristics\u003c/h2\u003e \u003cp\u003eKey characteristics of the studies, including their design, participant demographics, and methodological features, are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The following sections provide a narrative overview of these characteristics\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Participant Demographics and Study Details\u003c/h2\u003e \u003cp\u003eStudies were published between 2009 and 2021 in diverse geographical locations, including Europe, North America, Australia, and South America. Collectively, these studies included 739 participants, with a mean baseline age of 63.45 years and a nearly balanced sex distribution where women comprised approximately 47.5% of the total cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Clinical Measures and Comparison Groups\u003c/h2\u003e \u003cp\u003eAcross all included studies, the Mini-Mental State Examination (MMSE) was the standard tool used to confirm the diagnosis of Alzheimer's disease. To assess a broader range of cognitive functions, the studies employed a variety of other neuropsychological tests targeting various domains. In addition to the comparison with healthy controls, several studies included other clinical groups such as patients with early-onset Alzheimer's disease (EOAD) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), mild cognitive impairment (MCI) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), dementia with Lewy bodies (DLB) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), frontotemporal dementia (FTD) and semantic dementia (SD) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy population and characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eStudy\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCountry\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFollow-up Duration\u003c/em\u003e\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eStudy Design\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMain Population\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ePrimary Outcome\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eProgression measures (Biomarkers of Progression)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAdriaanse et al., 2014\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eThe Netherlands \u0026amp; USA\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2.5 years\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003elongitudinal\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eProgression\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAtrophy \u0026amp; Cortical Volume Loss\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eContador et al., 2021\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSpain \u0026amp; USA\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2 years\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003elongitudinal\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eEOAD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eOnset and Progression\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCortical Thickness and Gray Matter Volume\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHenneman et al., 2009\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eThe Netherlands \u0026amp; UK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1.8 years\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003elongitudinal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAD \u0026amp; MCI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eProgression\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eWhole Brain/Hippocampal Atrophy\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKrueger et al., 2010\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eUSA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e14 months\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003elongitudinal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAD \u0026amp; FTD \u0026amp; SD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eProgression\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAtrophy/Volume loss\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMak et al., 2015\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAustralia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1 year\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003elongitudinal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAD \u0026amp; DLB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eProgression\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eWhole Brain Atrophy\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVillemagne et al., 2013\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAustralia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3.8 years\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003elongitudinal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAD \u0026amp; MCI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eProgression\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eGray Matter Atrophy\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWeiler et al., 2015\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eItaly \u0026amp; Brazil\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e16\u0026thinsp;\u0026plusmn;\u0026thinsp;3 months\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003elongitudinal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eProgression\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eGray Matter Atrophy\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWeston et al., 2016\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eUK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2.9 years\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003elongitudinal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFAD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eOnset\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCortical Thickness\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Baseline Differences\u003c/h2\u003e \u003cp\u003eThis section details the baseline differences between patients with Alzheimer's disease (AD) and healthy controls (HC), presented separately for volumetric and cortical thickness metrics. Of the eight included studies, six provided data on baseline volumetric comparisons, while two reported on cortical thickness.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Volume Loss and Atrophy\u003c/h2\u003e \u003cp\u003eBaseline volumetric analyses consistently revealed significant brain atrophy in patients with Alzheimer's disease (AD) when compared to healthy controls (HC). A frequently reported finding was a reduction in the volume of hippocampus (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In a sample of individuals with early-onset AD (EOAD), Contador and his colleagues confirmed lower volumes in bilateral hippocampus and amygdala, which were accompanied by a corresponding increase in the volume of inferior-lateral ventricles (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA broader, distributed pattern of cortical atrophy was also described. Weiler and his team reported that at baseline, AD patients \u003cb\u003eexhibited\u003c/b\u003e a disrupted pattern of cortical atrophy, with significant involvement of medial temporal lobe, inferior parietal and postcentral gyri, and posterior cingulate, bilaterally (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Supporting this pattern of widespread atrophy, Adriaanse and his colleagues observed lower cortical volume in AD patients across a combined set of 'AD-signature regions' that included inferior frontal cortex, posterior cingulate cortex, temporal-polar cortex, lateral temporal cortex, inferior parietal sulcus, inferior parietal cortex, and medial temporal lobe (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, Kruger reported that baseline volumes were not significantly different between diagnostic groups in parietal and occipital lobes (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Their analysis focused on temporal lobe differences for differential diagnosis, noting that patients with semantic dementia (SD) had significantly different right temporal lobe volumes compared to controls, the volumetric difference was also significant when compared against both controls and patients with Alzheimer's disease (AD) in left temporal lobe (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Cortical Thickness\u003c/h2\u003e \u003cp\u003eBaseline differences in cortical thickness were a key finding in two studies that focused on early and genetic forms of Alzheimer's disease. In a study conducted by Contador and his colleagues, involving patients with early-onset Alzheimer's disease (EOAD), reduced cortical thickness was observed in both hemispheres compared to healthy controls. Following a vertex-wise analysis, a distinct pattern of thinning was highlighted, particularly in posterior regions, such as precuneus and the temporoparietal junction (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Similarly, Weston studied a sample with familial AD (FAD) and identified significantly reduced mean cortical thickness within a predefined 'cortical signature' comprising six regions: entorhinal cortex, inferior parietal cortex, precuneus, superior frontal cortex, superior parietal cortex, and supramarginal gyrus. These findings established that the differences were detectable up to three years before the predicted onset of symptoms, highlighting cortical thinning as a key pre symptomatic feature (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Longitudinal Changes\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Volume Loss and Atrophy\u003c/h2\u003e \u003cp\u003eLongitudinal analyses revealed that patients with Alzheimer's disease (AD) exhibit a significantly faster rate of brain atrophy compared to healthy controls (HC). Adriaanse reported that AD patients lose approximately 3.5% of cortical volume over 2.5 years, compared to just 0.6% in controls (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Similarly, Mak found that the annual rate of brain volume loss in AD patients is approximately\u0026thinsp;\u0026minus;\u0026thinsp;1.8%, which is double the rate observed in controls (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe topographical progression of this gray matter atrophy follows a distinct anatomical pattern. This process involves accelerated atrophy in multiple cortical lobes, including temporal, occipital, and parietal lobes, as well as periventricular regions (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), and key frontal areas such as inferior frontal and orbitofrontal cortices (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The atrophy also extends into subcortical structures, including thalamus, cerebellum, putamen, and caudate, with the most significant volume reductions observed in bilateral hippocampus, amygdala, and thalamus (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Furthermore, a more aggressive rate of atrophy has been associated with an earlier age of disease onset (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe specific topographical pattern of atrophy differs among dementia groups. Analyses by Krueger and his colleagues revealed that atrophy rates in frontal lobes of FTD patients were twice as high as those seen in AD patients; likewise, atrophy rates in temporal lobes of SD patients were double those of AD patients (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). While the topographical pattern of atrophy distinguished the groups, the overall rate of atrophy in AD was not significantly faster than other dementia groups (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The unique sensitivity of the hippocampus was highlighted by Henneman, such that among MCI patients, higher hippocampal atrophy rates were a strong predictor of progression to a clinical AD diagnosis (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). However, the same study noted that whole-brain atrophy rates were better at discriminating between patients with established AD and those with MCI, illustrating that the optimal biomarker may differ depending on the disease stage (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Cortical Thickness\u003c/h2\u003e \u003cp\u003eThe studies identified distinct, yet overlapping, topographical patterns of change. For instance, Weston defined a 'cortical signature' of six regions that showed significant thinning in familial AD (FAD) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The study then demonstrated that by monitoring the rate of change, a significantly faster rate of thinning could be detected in precuneus as early as eight years before the predicted onset of symptoms (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Complementing this, Contador found a posterior-dominant pattern of thinning in early-onset AD (EOAD), with most significant changes occurring in bilateral entorhinal, parahippocampal, and precuneus regions (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDetection of these changes in pre symptomatic stages was a key finding. Weston established that differences in cortical thickness were detectable as early as four years before the predicted onset of symptoms, first appearing in precuneus. Furthermore, they demonstrated that a lower baseline cortical thickness predicted a greater rate of subsequent thinning in FAD mutation carriers (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The importance of the rate of change was highlighted by Contador who found that regions with greater baseline thinning experienced more atrophy over a two-year follow-up period (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Progression and Onset Predictors\u003c/h2\u003e \u003cp\u003eFour of the included studies identified specific MRI measures that could predict progression or onset of Alzheimer\u0026rsquo;s disease (AD). The rate of atrophy was highlighted as a key dynamic marker. \u003cb\u003eKrueger\u003c/b\u003e stated that because the rate of regional atrophy correlates closely with the speed of clinical changes, it can serve as a valuable tool for monitoring disease progression (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Similarly, Henneman found that both a higher rate of hippocampal atrophy and a lower baseline hippocampal volume were strong, independent predictors of progression to AD in patients without a diagnosis of dementia at baseline (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). For patients already diagnosed with mild cognitive impairment (MCI), this study identified a low baseline hippocampal volume as the strongest predictor of future conversion to AD. Cortical thickness, on the other hand, characterizes the prodromal stage of the disease. Weston found that the earliest significant cortical thickness difference between groups appeared in the precuneus region, detectable up to four years before disease onset (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This early precuneus thinning may therefore represent a prodromal marker of Alzheimer\u0026rsquo;s disease onset, rather than an indicator of its progression.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAlzheimer\u0026rsquo;s Disease Progression study methodologies and main results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMeasures/tools\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMain results\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdriaanse et al., 2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.82 (6.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (14.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInferior-Frontal Cortex\u003c/p\u003e \u003cp\u003ePosterior Cingulate Cortex\u003c/p\u003e \u003cp\u003eTemporal Polar Cortex\u003c/p\u003e \u003cp\u003eLateral Temporal Cortex \u003c/p\u003e \u003cp\u003eInferior-Parietal Sulcus\u003c/p\u003e \u003cp\u003eMedial Temporal Lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eT-MRI scans\u003c/p\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003cp\u003eClinical and neuropsychological evaluations\u003c/p\u003e \u003cp\u003eCSF biomarker samples\u003c/p\u003e \u003cp\u003eAPOE genotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConnection between baseline [18F] FDG uptake in the precuneus and subsequent volume loss in Alzheimer's disease (AD) patients; lower metabolism at baseline is associated with greater cortical volume reduction over time. This relationship was absent in normal controls.\u003c/p\u003e \u003cp\u003eBaseline [11C] PIB binding did not correlate with cortical volume loss over time in either the AD group or the control group.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContador et al., 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.25 (4.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56 (56.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBilateral Entorhinal\u003c/p\u003e \u003cp\u003eRight Fusiform\u003c/p\u003e \u003cp\u003eLeft Precuneus\u003c/p\u003e \u003cp\u003eBilateral Parahippocampal Regions \u003c/p\u003e \u003cp\u003eBilateral Hippocampus\u003c/p\u003e \u003cp\u003eAmygdala\u003c/p\u003e \u003cp\u003eThalamus\u003c/p\u003e \u003cp\u003eCerebellum Gray Matter\u003c/p\u003e \u003cp\u003eLeft Caudate\u003c/p\u003e \u003cp\u003ePutamen Regions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMRI Neurologic and neuropsychological assessment\u003c/p\u003e \u003cp\u003eMMSE Clinical Dementia Rating scale\u003c/p\u003e \u003cp\u003eWechsler Abbreviated Scale of Intelligence\u003c/p\u003e \u003cp\u003eRecognition Memory Test\u003c/p\u003e \u003cp\u003eForward and backward digit span\u003c/p\u003e \u003cp\u003eGraded Naming Test\u003c/p\u003e \u003cp\u003eGraded Difficulty Arithmetic Test\u003c/p\u003e \u003cp\u003eVisual Object and Space Perception battery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLongitudinal analysis over two years indicated that progressive atrophy extended throughout the neocortex, exhibiting a posterior-to-anterior gradient and affecting areas beyond the initial subcortical structures. In early-onset Alzheimer's disease (EOAD), cortical thinning was more pronounced in posterior regions compared to healthy controls. Additionally, initial cerebrospinal fluid (CSF) A-Beta42 levels near the normality threshold were linked to accelerated cortical loss in these posterior areas in EOAD. Elevated baseline t-tau levels were associated with increased rates of volume loss in the medial temporal lobe subcortical structures among EOAD patients.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHenneman et al., 2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75 (52.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHippocampus\u003c/p\u003e \u003cp\u003eWhole Brain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClinical and neuropsychological evaluations\u003c/p\u003e \u003cp\u003eMRI assessments\u003c/p\u003e \u003cp\u003eCambridge Cognitive Examination (CAMCOG)\u003c/p\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003cp\u003eUnified Parkinson3s Disease Rating Scale Part III\u003c/p\u003e \u003cp\u003eNeuropsychiatric Inventory\u003c/p\u003e \u003cp\u003eCognitive fluctuation scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIn individuals without dementia, regional hippocampal measures were the most significant indicators of progression to Alzheimer's disease (AD). Additionally, in this group, the rate of whole brain atrophy offered an independent predictive value, enhancing the prediction beyond what hippocampal measures alone could achieve. In patients with mild cognitive impairment (MCI), initial hippocampal atrophy emerged as the most robust predictor of progression to AD.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKrueger et al., 2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27 (39.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eParietal Lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClinical and neuropsychological assessments\u003c/p\u003e \u003cp\u003eMRI (VBM/ TBM/ DT MRI)\u003c/p\u003e \u003cp\u003eCSF sample by lumbar puncture\u003c/p\u003e \u003cp\u003eFDG PET\u003c/p\u003e \u003cp\u003eSPECT\u003c/p\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003cp\u003eProse memory test\u003c/p\u003e \u003cp\u003eVerbal and spatial span\u003c/p\u003e \u003cp\u003eRey\u0026rsquo;s Figure Delayed Recall Test\u003c/p\u003e \u003cp\u003eRey\u0026rsquo;s Figure Copy Test\u003c/p\u003e \u003cp\u003eRaven\u0026rsquo;s Coloured Progressive test\u003c/p\u003e \u003cp\u003eAttentive matrices\u003c/p\u003e \u003cp\u003ePhonemic and Semantic Fluency tests\u003c/p\u003e \u003cp\u003eToken Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe rate of volume loss is specific to each disease and is more pronounced in regions that have the smallest baseline volume. In the AD group, the highest rate of atrophy change was observed in the parietal lobes. Additionally, the atrophy rates in groups with SD and FTD were twice as high as those observed in the AD group.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMak et al., 2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.8 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24 (33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeriventricular areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRecord of medical history\u003c/p\u003e \u003cp\u003eNeuropsychological testing (MMSE etc.)\u003c/p\u003e \u003cp\u003ePhysical and neurological examination\u003c/p\u003e \u003cp\u003eScreening laboratory tests\u003c/p\u003e \u003cp\u003eMRI\u003c/p\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOver a one-year period, the Alzheimer's disease (AD) group exhibited a significantly higher rate of atrophy compared to the dementia with Lewy bodies (DLB) group. The DLB group demonstrated an atrophy rate similar to that of healthy controls (HC). Among subjects with dementia, a younger age at baseline was associated with a more aggressive rate of atrophy. The AD group showed an increased whole brain atrophy rate in comparison to HC.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVillemagne et al., 2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.8 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96 (48.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHippocampus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026sup1;\u0026sup1;C-PiB PET\u003c/p\u003e \u003cp\u003eMRI scan\u003c/p\u003e \u003cp\u003eNeuropsychological examination\u003c/p\u003e \u003cp\u003eRey complex figure test\u003c/p\u003e \u003cp\u003eCalifornia verbal learning test second edition (CVLT-II)\u003c/p\u003e \u003cp\u003eBoston naming test\u003c/p\u003e \u003cp\u003eAPOE Genotype\u003c/p\u003e \u003cp\u003eClinical dementia rating\u003c/p\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe rate of Aβ deposition appears to decrease at higher Aβ burdens and lower MMSE scores, indicating that Aβ deposition slows during the later stages of AD. Additionally, a higher baseline level of Aβ is predictive of the subsequent rate of brain atrophy over time. Hippocampal atrophy is projected to occur significantly earlier than the clinical symptoms of dementia, specifically approximately 4.2 years prior to the onset of dementia.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeiler et al., 2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.4 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24 (47.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedial Temporal Lobe \u003c/p\u003e \u003cp\u003eInferior Parietal Lobe \u003c/p\u003e \u003cp\u003ePostcentral Gyri \u003c/p\u003e \u003cp\u003eBilateral Posterior Cingulum \u003c/p\u003e \u003cp\u003eHippocampus\u003c/p\u003e \u003cp\u003eLateral Temporal Cortex\u003c/p\u003e \u003cp\u003eTemporal Pole \u003c/p\u003e \u003cp\u003eInferior Frontal \u0026amp; Orbitofrontal Cortices \u003c/p\u003e \u003cp\u003eInferior \u0026amp; Superior Parietal Lobe \u003c/p\u003e \u003cp\u003ePosterior Cingulate Cortex\u003c/p\u003e \u003cp\u003eCerebellum\u003c/p\u003e \u003cp\u003eBilateral Putamen \u003c/p\u003e \u003cp\u003eLeft Inferior Cingulate Cortex \u003c/p\u003e \u003cp\u003eInsula \u003c/p\u003e \u003cp\u003eRight Caudate Nucleus \u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMRI\u003c/p\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAt baseline, patients with Alzheimer's disease (AD) exhibited gray matter atrophy and white matter microstructural damage in regions including the medial temporal, posterior cingulate, and inferior parietal cortices, as well as the corpus callosum and cingulum. Over the follow-up period, both gray matter atrophy and alterations in white matter microstructure continued to progress. Notably, the accumulation of white matter damage was not associated with baseline gray matter or with gray matter tissue loss over time. Furthermore, AD patients with higher levels of cerebrospinal fluid (CSF) t-tau at baseline demonstrated a greater progression of white matter damage\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeston et al., 2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.5 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52 (61.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEntorhinal Cortex \u003c/p\u003e \u003cp\u003eInferior Parietal Cortex\u003c/p\u003e \u003cp\u003ePrecuneus\u003c/p\u003e \u003cp\u003eSuperior Frontal Cortex \u003c/p\u003e \u003cp\u003eSuperior Parietal Cortex\u003c/p\u003e \u003cp\u003eSupramarginal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMRI\u003c/p\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003cp\u003eMedical history\u003c/p\u003e \u003cp\u003eNeuropsychological and neurologic examination\u003c/p\u003e \u003cp\u003ePetersen criteria for the MCI\u003c/p\u003e \u003cp\u003eVisual association test\u003c/p\u003e \u003cp\u003eCategory fluency test\u003c/p\u003e \u003cp\u003eInstrumental Activities of Daily Living Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCortical thickness was considerably lower in six key regions including the entorhinal cortex, the inferior parietal cortex, the precuneus, the superior frontal cortex, the superior parietal cortex, and the supramarginal gyrus in mutation carriers 3 years before the onset of cognitive symptoms.\u003c/p\u003e \u003cp\u003eThe precuneus is among the first regions to exhibit a significant reduction in cortical thickness, occurring approximately four years prior to the onset of symptoms.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis systematic review examines longitudinal MRI studies to provide a new spatio-temporal perspective on grey matter degeneration in Alzheimer's disease. To the best of our knowledge, this is the first study to differentiate between cortical thickness and grey matter atrophy as separate biomarkers, each serving a distinct predictive purpose. We observed a progressive degeneration across distinct anatomical and clinical stages, from presymptomatic cortical thinning to widespread cortico-subcortical atrophy. Given the limitations of static imaging techniques (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), we emphasize the importance of repeated biomarker assessments over time (\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). We refer to this approach as a dynamic spatio-temporal biomarker and propose its usefulness for early detection and management of Alzheimer's disease progression.\u003c/p\u003e \u003cp\u003eA key finding of this review with significant clinical implications is the distinct utility of cortical thickness versus volumetric atrophy as stage-dependent biomarkers. The data show that cortical thinning, particularly its rate of change, serves as a sensitive prodromal marker, detectable years before the predicted onset of symptoms. In contrast, volumetric atrophy has a unique spatio-temporal signature in established AD and is more indicative of disease progression, with whole-brain atrophy rates effectively discriminating between MCI and AD stages. A thorough understanding of this distinction, where cortical thinning signals the onset and atrophy rates track the progression, is a productive step toward improving disease management and designing stage-appropriate clinical trials.\u003c/p\u003e \u003cp\u003eThe earliest structural changes identified in longitudinal studies occur during the presymptomatic phase. Accelerated cortical thinning, particularly in posterior brain regions such as the precuneus, posterior cingulate, and entorhinal cortex, emerges several years before cognitive symptoms develop (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Notably, Weston demonstrated that cortical thinning of precuneus in familial AD could be detected up to eight years prior to predicted symptom onset (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). These findings align with amyloid accumulation timelines (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and support the conceptualization of AD as a protracted continuum, with subtle structural degeneration preceding clinical manifestation (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). However, while cortical thinning is a sensitive early marker, the utility of structural MRI for detecting preclinical AD in routine practice remains limited, mainly due to overlap with normative aging and the subtlety of changes at this stage (\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs the disease advances to the stage of mild cognitive impairment (MCI), hippocampal atrophy becomes more prominent. While static hippocampal volume has demonstrated moderate sensitivity as a diagnostic feature (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), longitudinal studies reveal that the rate of hippocampal atrophy is a stronger predictor of clinical progression (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This dynamic measure consistently outperforms whole-brain atrophy in predicting the transition from MCI to AD (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), underscoring the importance of tracking atrophy rates rather than relying solely on baseline measurements.\u003c/p\u003e \u003cp\u003eThis review confirms that hippocampal degeneration represents a critical pathological milestone, coinciding with the onset of clinically detectable memory impairment. Standardized segmentation and consistent imaging protocols remain essential to improve cross-study comparability (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn established AD, neurodegeneration extends beyond medial temporal lobes, involving a broader cortical and subcortical network. The atrophy pattern typically spreads from temporal lobes to parietal and occipital regions, contributing to visuospatial and broader cognitive deficits (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Degeneration in deep gray matter structures\u0026mdash;including amygdala, thalamus, caudate, and putamen\u0026mdash;further emphasizes that AD is a whole-brain disorder, rather than limited to cortical regions (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe distinct topographical pattern of atrophy holds a significant value for differential diagnosis. Studies show that the highest rate of atrophy in AD occurs in parietal lobes. In contrast, Frontotemporal Dementia (FTD) primarily involves atrophy of frontal lobe, while Semantic Dementia (SD) has a temporal lobe focus (Krueger et al., 2010). Furthermore, compared to dementia with Lewy bodies (DLB), AD is marked by accelerated global volume loss, particularly in periventricular areas (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), confirming MRI's role in distinguishing between dementia subtypes.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eLongitudinal structural MRI studies consistently demonstrate that Alzheimer\u0026rsquo;s disease (AD) progresses in a spatiotemporal pattern: beginning with subtle cortical thinning in posterior brain regions during the presymptomatic phase, followed by hippocampal atrophy during progression to mild cognitive impairment (MCI), and culminating in widespread cortico-subcortical and white matter degeneration in established dementia. Among structural biomarkers, hippocampal atrophy rates and cortical thinning trajectories show the strongest predictive value for clinical progression, outperforming static measures.\u003c/p\u003e \u003cp\u003eOverall, MRI-based structural biomarkers offer a non-invasive window into the temporal evolution of AD pathology, supporting their integration into early detection frameworks, individualized prognostic models, and ongoing disease monitoring. However, their clinical utility depends on improved standardization, multimodal integration, and further validation across diverse populations.\u003c/p\u003e"},{"header":"6. Limitations","content":"\u003cp\u003eThis systematic review provides important insights into Alzheimer\u0026rsquo;s disease (AD) progression using longitudinal MRI markers, yet several limitations should be acknowledged. The review primarily focuses on cortical thickness and brain atrophy, excluding other potentially valuable MRI features such as white matter integrity and functional changes, which could have expanded the findings. Additionally, it focuses solely on structural MRI, leaving out multimodal imaging modalities like PET that might provide a more comprehensive understanding of AD pathology.\u003c/p\u003e \u003cp\u003eThe inclusion criteria and search strategy may have inadvertently excluded relevant studies, particularly non-English publications, potentially introducing selection bias. The reliance on published data further amplifies the risk of publication bias, as studies with null findings may be underrepresented. The variability in methodologies, imaging protocols, and outcome measures across the included studies posed challenges for synthesis and may have affected the robustness of conclusions. Finally, while subgroup analyses such as early-onset versus typical AD were considered, demographic factors like age, sex, and genetic predispositions were not extensively explored, limiting the nuance of the findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this manuscript, the authors utilized Google\u0026apos;s Gemini generative AI to enhance grammar, phrasing, and overall readability. All suggestions were critically reviewed by the authors, who retain full responsibility for the accuracy and integrity of the work presented.\u003c/p\u003e\n\u003cp\u003eAuthors contribution\u003c/p\u003e\n\u003cp\u003eM.L. and M.Sa. wrote the manuscript. M.L. and F.H. performed data extraction and Risk of Bias assessment. M.L. designed the tables. M.L., M.B., B.A., F.H., P.M., and M.So. screened the paper pool. A.Z. supervised the entire project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u0026nbsp;\u003c/strong\u003estatement\u003c/p\u003e\n\u003cp\u003eThe authors report there are no competing interests to declare\u003c/p\u003e\n\u003cp\u003eBiographical note\u003cbr\u003eMohammad saemi:\u003cbr\u003eAfter an insightful bachelor\u0026rsquo;s experience in psychology, I became unsatisfied with the lack of an objective perspective on the human mind within the field. At the same time, I grew increasingly fascinated by the nature of academic research, which led me to pursue a career in cognitive science. My goal is to gain a deeper understanding of the human brain through advanced neuroimaging and neuromodulatory technologies.\u003c/p\u003e\n\u003cp\u003eMy main research interest focuses on time perception and its relationship with various mental disorders, approached through a dynamic complex network perspective on the brain and explored with more nuanced methods of effective connectivity. As a lecturer, I have taught the fundamentals of image processing using the Statistical Parametric Mapping (SPM) package. I also consider myself a transhumanist, aspiring to advance our cognitive capacities through what the literature describes as cognitive enrichment.\u003c/p\u003e\n\u003cp\u003eMasoud Lotfalipour:\u003c/p\u003e\n\u003cp\u003eMasoud Lotfalipour is a master\u0026rsquo;s student in Cognitive Science at the Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran. His research focuses on neuroimaging and brain connectivity, with particular interest in neurodegenerative disorders such as Alzheimer\u0026rsquo;s disease and Parkinson\u0026rsquo;s disease. He has experience working with multimodal datasets, including structural MRI, fMRI, and DTI, and applies advanced computational and statistical methods to investigate brain changes associated with aging and disease progression.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Maryam Sadat Banifatemeh:\u003c/p\u003e\n\u003cp\u003eMaryam Sadat Banifatemeh holds an M.A. in Clinical Psychology from Allameh Tabataba\u0026rsquo;i University, Tehran, Iran. Her research interests include neuropsychology, executive brain functions, and the cognitive and neural mechanisms underlying psychiatric disorders, with applications in evidence-based psychotherapeutic interventions. She is currently conducting various applied, quantitative, and longitudinal clinical studies examining the relationships between brain function, cognition, and mental health.\u003c/p\u003e\n\u003cp\u003eFatemeh Hemmat:\u003c/p\u003e\n\u003cp\u003eFatemeh Hemmat is a graduate of Russian linguistics from faculty of foreign languages and literature, University of Tehran, Tehran, Iran. She is interested in how mind and brain function to produce and comprehend language in healthy and impaired populations.\u003c/p\u003e\n\u003cp\u003eMoslem Solhirad:\u003c/p\u003e\n\u003cp\u003eWith some efforts and large passion to relationship between Science and Philosophy, Moslem eagers to follow implications in Science for better understanding of Experimental Issues in Philosophy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBahareh Amini:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBahareh holds an M.A. in Genetics and has recently completed her B.A. in Psychology. Her research interests include neuropsychology and clinical psychology, and she is about to begin her M.A. in Clinical Psychology. She is eager to deepen her understanding of the brain and its impact on human behavior and everyday life.\u003c/p\u003e\n\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.\u003c/p\u003e\n\u003cp\u003eAdditional information\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research, authorship, and publication of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGreenblat C (2025) Fact sheet: Dementia [Internet] Geneva: World Health Organization; [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/en/news-room/fact-sheets/detail/dementia\u003c/span\u003e\u003cspan address=\"https://www.who.int/en/news-room/fact-sheets/detail/dementia\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e(2025) Alzheimer's disease facts and figures. 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Biomarkers Neuropsychiatry 8:100063\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePorsteinsson AP, Isaacson RS, Knox S, Sabbagh MN, Rubino I (2021) Diagnosis of Early Alzheimer's Disease: Clinical Practice in 2021. J Prev Alzheimers Dis 8(3):371\u0026ndash;386\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFjell AM, McEvoy L, Holland D, Dale AM, Walhovd KB (2014) What is normal in normal aging? Effects of aging, amyloid and Alzheimer's disease on the cerebral cortex and the hippocampus. Prog Neurobiol 117:20\u0026ndash;40\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToepper M (2017) Dissociating Normal Aging from Alzheimer's Disease: A View from Cognitive Neuroscience. J Alzheimers Dis 57(2):331\u0026ndash;352\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArrondo P, El\u0026iacute;a-Zudaire \u0026Oacute;, Mart\u0026iacute;-Andr\u0026eacute;s G, Fern\u0026aacute;ndez-Seara MA, Riverol M (2022) Grey matter changes on brain MRI in subjective cognitive decline: a systematic review. Alzheimers Res Ther 14(1):98\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLombardi G, Crescioli G, Cavedo E, Lucenteforte E, Casazza G, Bellatorre AG et al (2020) Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment. Cochrane Database Syst Rev 3(3):Cd009628\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu X, Meier M, Pruessner J (2023) Challenges and opportunities of diagnostic markers of Alzheimer's disease based on structural magnetic resonance imaging. Brain Behav 13(3):e2925\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Leon MJ, Mosconi L, Blennow K, DeSanti S, Zinkowski R, Mehta PD et al (2007) Imaging and CSF studies in the preclinical diagnosis of Alzheimer's disease. Ann N Y Acad Sci 1097:114\u0026ndash;145\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Oostveen WM, de Lange ECM (2021) Imaging Techniques in Alzheimer's Disease: A Review of Applications in Early Diagnosis and Longitudinal Monitoring. Int J Mol Sci. ;22(4)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e This is an average value.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Alzheimer’s disease, brain atrophy, magnetic resonance imaging, structural biomarkers, cortical thickness","lastPublishedDoi":"10.21203/rs.3.rs-9688545/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9688545/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003eThis systematic review synthesizes evidence from longitudinal MRI studies to evaluate how cortical thickness and brain atrophy serve as distinct biomarkers for Alzheimer's disease (AD). The goal is to differentiate their specific roles in tracking disease onset versus progression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e Following PRISMA guidelines, a systematic search of four major databases was conducted for longitudinal MRI studies of structural brain changes in AD. From an initial 10,908 records, eight studies were selected based on pre-defined eligibility criteria, including the presence of an AD patient group and a healthy control comparison. The quality of the included studies was systematically assessed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003eThe findings reveal distinct, stage-dependent roles for MRI biomarkers. Cortical thinning serves as a sensitive early marker, detectable years before clinical symptoms, making it an indicator of disease onset. In contrast, volumetric atrophy and its rate of change are robust markers of disease progression in established AD. The disease follows a predictable pattern of degeneration, beginning with cortical thinning and advancing to widespread atrophy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e Longitudinal MRI analysis confirms that cortical thinning and volumetric atrophy are distinct biomarkers for different stages of AD. Cortical thinning signals disease onset, while the rate of atrophy tracks its progression. This dynamic approach positions MRI as a valuable non-invasive tool for disease monitoring, though clinical utility depends on improved standardization.\u003c/p\u003e","manuscriptTitle":"Longitudinal study of Alzheimer's disease progression through MRI markers: A Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-14 15:27:46","doi":"10.21203/rs.3.rs-9688545/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"33b82223-66ab-4266-842f-2c1e24bcda77","owner":[],"postedDate":"May 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67997284,"name":"Cognitive Neuroscience"},{"id":67997285,"name":"Nuclear Medicine \u0026 Medical Imaging"}],"tags":[],"updatedAt":"2026-05-14T15:27:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-14 15:27:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9688545","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9688545","identity":"rs-9688545","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}