Hippocampal atrophy in Alzheimer’s disease

Using neuroimaging to inform clinical practice for the diagnosis and treatment of mild cognitive impairment

Advances in structural and functional neuroimaging techniques have unquestionably improved understanding of the development and progression of Alzheimer disease (AD), with evidence supporting regional (and network) change that underlies cognitive decline across the "healthy" aging/mild cognitive impairment (MCI)/AD spectrum. This review focuses on visual rating scales and volumetric analyses that could be easily integrated into clinical practice, followed by a review of functional neuroimaging findings suggesting that widespread cerebral dysfunction underlies the learning and memory deficits in MCI. Evidence of preserved neuroplasticity in this population and that cognitive rehabilitation techniques may capitalize on this plasticity to improve cognition in those with MCI is also discussed.

The PredictAD project: development of novel biomarkers and analysis software for early diagnosis of the Alzheimer's disease

Alzheimer's disease (AD) is the most common cause of dementia affecting 36 million people worldwide. As the demographic transition in the developed countries progresses towards older population, the worsening ratio of workers per retirees and the growing number of patients with age-related illnesses such as AD will challenge the current healthcare systems and national economies. For these reasons AD has been identified as a health priority, and various methods for diagnosis and many candidates for therapies are under intense research. Even though there is currently no cure for AD, its effects can be managed. Today the significance of early and precise diagnosis of AD is emphasized in order to minimize its irreversible effects on the nervous system. When new drugs and therapies enter the market it is also vital to effectively identify the right candidates to benefit from these. The main objective of the PredictAD project was to find and integrate efficient biomarkers from heterogeneous patient data to make early diagnosis and to monitor the progress of AD in a more efficient, reliable and objective manner. The project focused on discovering biomarkers from biomolecular data, electrophysiological measurements of the brain and structural, functional and molecular brain images. We also designed and built a statistical model and a framework for exploiting these biomarkers with other available patient history and background data. We were able to discover several potential novel biomarker candidates and implement the framework in software. The results are currently used in several research projects, licensed to commercial use and being tested for clinical use in several trials.