The thalamus: a key inodulator of cognitive resilience and conversion from rodromal to clinical stages of the Alzheimer's Disease spectrum
Progetto 4.1 Abstract Alzheimer's disease (AD) is the most common age-related dementia. AD is
preceded by a prodromal stage known as Mild Cognitive Impairment (MCI), representing a crucial crossroad in disease development and clinical progression. Early identification of the brain changes associated with MCI is critical to catch the disease at its initial stage, unraveling its pathogenic mechanisms, and helping design more effective therapeutic interventions. Current models posit that the brain, under the early attach of AD pathology, responds with an adaptive enhancement of the functional network connections. However, the neural substrates and mechanisms of the phenomenon are still poorly understood.
Interestingly, recent evidence indicates that the thalamus critically modulates the functional interactions in the cortex and that focal thalamic lesions produce distal effects on brain network organization. Moreover, post- mortem studies on AD patients revealed a hierarchical evolution of pathology across the thalamic nuclei, most consistently and severely in the limbic thalamus. Anatomically and functionally, the limbic thalamus is closely connected with brain regions primarily affected by AD pathology and whose impairment has been associated with the onset of AD core symptoms. In this project, we will test the hypothesis that the AD-related (structural and functional) alteration of the limbic thalamus shapes individual resilience and predict the clinical progression of MCI subjects to AD. The timeline of these processes will be investigated.
To this aim, we will use an ongoing, multicenter platform of AD
Neuroimaging Initiative, which provides, longitudinally, multimodal neuroimaging raw data, demographic/neuropsychological/clinical information, and liquor biomarkers. A novel approach for thalamic nuclei parcellation will be applied to define thalamic regions of interest (ROIs). The combined analysis of anatomical Magnetic Resonance (MR) and diffusion- weighted images will provide information on the macro- and micro- structural integrity of thalamic ROIs. Resting state-functional MR images
will be processed to offer information on thalamic and cortical functioning.
Positron Emission Tomography images will be analyzed to map the whole- brain spreading of neuropathological hallmarks of AD. Machine learning algorithms will determine which MRI-derived thalamic markers are more
predictive of the clinical outcomes and cognitive performances in MCI patients. This project aims to generate a better definition of the AD pathophysiological mechanisms and improve diagnostic strategies by identifying neural signatures of MCI subjects at risk of AD progression.