Dementia is associated with a variety of neurovascular and neurometabolic abnormalities. Traditional imaging techniques used to investigate such abnormalities, such as Positron Emission Tomography and functional Magnetic Resonance Imaging, are not always well tolerated, have expensive start up and running costs, and are limited with regards to the types of experiments that can be performed as they can be highly sensitive to movement, are noisy, and have physical restrictions.

Near-infrared spectroscopy (NIRS) is a non-invasive neuroimaging technique which uses light in the near-infrared spectrum to detect relative changes in concentration of oxygenated and deoxygenated haemoglobin, and the oxidation state of Cytochrome C Oxidase. As such, NIRS can provide measures of brain oxygenation and metabolism. NIRS is less sensitive to movement, is well tolerated and has few contraindications. It is thus a promising candidate for use in clinics or in peoples' homes for monitoring dementia.

In the present study, the investigators aim to use both dual-wavelength and broadband NIRS in a range of dementia subtypes, including Alzheimer's Disease and Dementia with Lewy Bodies, and severities, including Mild Cognitive Impairment, to identify how brain oxygenation and metabolism is altered in dementia and across various clinical subgroups. The investigators also aim to determine the relationship between brain oxygenation and metabolism in dementia, and use machine learning approaches to identify optical biomarkers for dementia.

There are several different types of dementia including Alzheimer's Disease (AD) and Dementia with Lewy Bodies (DLB). Due to the overlapping symptomatology across types of dementia and the lack of objective biomarkers currently available for dementia, misdiagnosis rates are high. Additionally, the transition from what is commonly thought to be an intermediate stage, termed Mild Cognitive Impairment (MCI), to dementia, is not well defined. Neurovascular and metabolic dysfunction has been strongly linked to neurodegeneration and dementia, however, a mechanistic understanding of this link has not been fully developed.

Near-Infrared Spectroscopy (NIRS) is a non-invasive, non-ionising and portable neuroimaging technique which uses light to quantify changes in concentration of oxygenated and deoxygenated haemoglobin in the brain. As such, it is a highly attractive alternative to functional Magnetic Resonance Imaging as it allows access to a larger variety of individuals, can be used at the bedside or in patients' own homes, and is significantly less intrusive.

To identify how the brain's haemodynamics and metabolism is altered in dementia, this study will use NIRS in 25 patients with AD, 25 patients with DLB, 50 patients with MCI and 100 age-matched healthy controls. This study will be conducted by the School of Technology and the School of Clinical Medicine at the University of Cambridge.

Firstly, the investigators will perform several cognitive tests in these patient groups whilst measuring brain activity using a state-of-the-art, dual-wavelength, high-density NIRS device to map how the brain's haemodynamics are altered in dementia. Secondly, the investigators will perform further cognitive tests using broadband NIRS to measure how neurometabolism is altered across the patient groups. The investigators will also relate the optical data to several facets of cognition that these cognitive tests will measure including memory, attention, and motor function. Several questionnaires will also be administered to assess non-cognitive symptoms such as depression and sleep disturbances. If participants in the patient groups have not had a Magnetic Resonance Imaging (MRI) scan, or did one over two years ago, they will also undertake an MRI scan to enable the localisation of brain activity, measured by NIRS, accounting for individual differences in brain structure and atrophy patterns.

The investigators shall compare all patient groups (AD, DLB, MCI) with healthy controls to determine how the brain's haemodynamics and metabolism are altered in dementia, as well as how this relates to both behavioural scores (collected during cognitive testing) and clinical scores (using either data collected from questionnaires or patient's own clinical history). Through combining the two NIRS techniques, the investigators shall also determine the nature of the relationship between the blood oxygenation in the surrounding vasculature and the intra-neuronal metabolic activity, and how this relationship may be altered in dementia and across different types of dementia. Additionally, the investigators shall apply computational methods, such as machine learning, to identify haemodynamic and/or metabolic signatures for use as biomarkers in the clinic.

Inclusion Criteria:

1. A diagnosis of probable:

   • Lewy Body Dementia
   • Alzheimer's Disease
   • Mild Cognitive Impairment (MCI-LB or MCI-AD) OR

2. Cognitively normal for their education and age, with a MMSE score above 26 AND

   • A good grasp of the English language
   • An informant (either a carer or family member) who will be available throughout testing (only relevant if in a patient group)

Exclusion Criteria:

1. Severe dementia

   • Unable to participate
   • A MMSE score below 12

2. A condition which influences metabolism or haemodynamics

   • Such as metabolic or respiratory disorders

3. A significant mental illness

   • Such as rheumatoid arthritis, systemic lupus erythematosus
   • Oral steroid use

4. A significant psychiatric disorder

5. MCI due to other causes such as traumatic brain injury, vascular dementia, or fronto-temporal dementia

6. A history of excessive drug or alcohol use

7. Contraindications to MRI (only for patient groups undertaking the MRI scan: AD/MCI/DLB)

   • Surgical implants e.g. pacemakers
   • Obesity

The target study population is people with dementia. This includes those with Alzheimer's Disease, Dementia with Lewy Bodies, as well as those in the intermediate stages of the disease, such as Mild Cognitive Impairment. We will also recruit healthy controls.