Alzheimer's disease causes progressive memory and cognitive decline, driven in part by the buildup of a protein called β-amyloid in the brain. New antibody therapies - lecanemab and donanemab - can remove amyloid and slow down the disease in its early stages. However, it is still unclear how long each patient should continue treatment or when it is safe to stop, because amyloid is cleared at different rates across individuals. Today, amyloid Positron Emission Tomography (PET) scans are used to measure whether amyloid has been removed from the brain, but these scans are expensive, not always available, and expose patients to radiation. Since repeated PET scans are not ideal, doctors need better ways to monitor treatment progress. This study will use advanced brain Magnetic Resonance Imaging (MRI) and blood tests to create personalized prediction models. These models will simulate how amyloid spreads or clears in each person's brain and help identify when treatment is still needed. With this approach, monitoring becomes safer, more efficient, and more affordable - helping ensure that each patient receives the right treatment for the right amount of time. This prospective monocenter study investigates the role of 3Tesla MRI-based predictive modeling in predicting treatment response to anti-amyloid monoclonal antibodies (lecanemab or donanemab administered as clinical practice) in 50 patients with early Alzheimer's disease (AD) at IRCCS Ospedale San Raffaele (Milan, Italy). Advanced MRI techniques, including high- resolution structural imaging for cortical thickness and volumetric atrophy, diffusion imaging for structural connectivity, and resting-state functional MRI for functional network analysis, will be acquired at baseline, 6, 12, and 18 months. These multimodal MRI measures will be integrated into computational approaches, such as the Aggregation Network Diffusion (AND) model, to simulate individual disease trajectories and predict the probability of achieving negativity at amyloid PET under treatment. While serial \[¹⁸F\]Flutemetamol PET will be performed as part of standard clinical practice to confirm amyloid removal, the focus of the study is on developing MRI- derived predictive biomarkers. The ultimate goal is to establish robust, non-invasive models capable of guiding individualized treatment monitoring and supporting evidence-based decisions on treatment discontinuation Overall, the project aims to support more precise care for people with early Alzheimer's disease, while reducing unnecessary procedures and improving quality of life.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
OTHER
Masking
NONE
Enrollment
50
Participants will undergo non-contrast-enhanced 3T MRI, including structural, diffusion, and functional sequences, to assess brain atrophy, connectivity, and other imaging markers relevant to disease progression and treatment response. Peripheral venous blood will be collected at scheduled study visits to measure plasma biomarkers associated with amyloid, tau, and neurodegeneration, providing complementary information on treatment effects through a minimally invasive method. Eventually, multimodal predictive models, using the Aggregation Network Diffusion (AND) model, based on baseline amyloid burden, structural and functional brain connectivity, and clinical, cognitive and plasma biomarkers will be developed to estimate the time to significant amyloid reduction in patients with MCI or mild AD treated with lecanemab or donanemab
San Raffaele Neurology Unit
Milan, Milano, Italy
Predicting time (in months) to amyloid [¹⁸F]Flutemetamol (amyloid) PET negativity on a single scan
Predicting time (in months) to amyloid \[¹⁸F\]Flutemetamol (amyloid) PET negativity, defined as amyloid load \<11 Centiloids on a single scan
Time frame: baseline, 6 months, 12 months and 18 months
Predicting time (in months) to amyloid [¹⁸F]Flutemetamol (amyloid) PET negativity on two consecutive scans
Predicting time (in months) to amyloid \[¹⁸F\]Flutemetamol (amyloid) PET negativity, defined as amyloid load -\<25 Centiloids on two consecutive scans
Time frame: Baseline, 6 months, 12 months, 18 months
Change in regional cerebral perfusion expressed in Standardized Uptake Volume Ratio (SUVR)
Change in regional cerebral perfusion expressed in SUVR (as a proxy from early-phase \[¹⁸F\]Flutemetamol - amyloid PET)
Time frame: 6 months, 12 months, 18 months
Change in global cerebral perfusion expressed in Standardized Uptake Volume Ratio (SUVR)
Change in global cerebral perfusion expressed in SUVR (as a proxy from early-phase \[¹⁸F\]Flutemetamol - amyloid PET)
Time frame: 6 months, 12 months, 18 months
Longitudinal change in brain volume
Evaluating brain volume changes over time
Time frame: 6 months, 12 months, 18 months
Longitudinal change in white matter integrity via Neurite Orientation Dispersion and Density Imaging (NODDI)
Evaluating structural white matter integrity over time
Time frame: 6 months, 12 months, 18 months
Longitudinal change in brain connectivity via functional MRI
Evaluating functional brain changes over time with whole brain statistics analysis
Time frame: 6 months, 12 months, 18 months
Change from baseline in plasma biomarkers Aβ42/Aβ40
Evaluating the correlation between plasma biomarkers with imaging-based measures of amyloid reduction and brain network alterations.
Time frame: 6 months, 12 months, 18 months
Change from baseline in plasma biomarkers pTau217
Evaluating the correlation between plasma biomarkers with imaging-based measures of amyloid reduction and brain network alterations.
Time frame: 6 months, 12 months, 18 months
Changes from baseline in plasma biomarkers pTau181
Evaluating the correlation between plasma biomarkers with imaging-based measures of amyloid reduction and brain network alterations.
Time frame: 6 months, 12 months, 18 months
Changes from baseline in plasma biomarkers NfL
Evaluating the correlation between plasma biomarkers with imaging-based measures of amyloid reduction and brain network alterations.
Time frame: 6 months, 12 months, 18 months
Changes from baseline in plasma biomarkers GFAP
Evaluating the correlation between plasma biomarkers with imaging-based measures of amyloid reduction and brain network alterations.
Time frame: 6 months, 12 months, 18 months
Change from baseline in Mini Mental Score examination (MMSE)
Neuropsychological assessment aimed at evaluating changes in cognitive performance and their association with neuroimaging and biomarker trajectories
Time frame: 6 months, 12 months, 18 months
Change from baseline in Alzheimer's Disease Assessment Scale's cognitive score (ADAS-Cog)
Change in ADAS-Cog's score and its association with neuroimaging and biomarker trajectories
Time frame: 6 months, 12 months, 18 months
Change from baseline in Clinical Dementia's Rating scale's cognitive score (CDR)
Change in CDR's score and its association with neuroimaging and biomarker trajectories
Time frame: 6 months, 12 months, 18 months
Change from baseline in Clinical Dementia Rating-Sum of Boxes' cognitive score (CDRsb)
Change in CDRsb's score and its association with neuroimaging and biomarker trajectories
Time frame: 6 months, 12 months, 18 months
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