Dementia with Lewy bodies (DLB) is the second most common cause of dementia and is associated with parkinsonism, hallucinations, and cognitive fluctuations. Diagnosis is often either missed or delayed due to physician lack of familiarity with characteristic features, the inability of structural MRI to detect a pathological signature for this condition, and the lack of healthcare provider access to "indicative biomarkers" that are either unavailable at community clinics or costly due to lack of insurance coverage. The role of resting state function MRI (rs-fMRI) as a diagnostic biomarker has been underexplored in this disease. We propose using a novel cloud-based automated imaging software processing program that identifies abnormal brain networks or connectomes using resting state functional MRI (rs-fMRI) and data from the Human Connectome Project (HCP). Furthermore, the imaging protocol to capture this data is relatively short (15 minutes) and can be performed at most imaging centers, lending potential clinical applicability to this study. We intend to study dysfunctional large scale brain networks (LSBNs) in DLB by comparing rs-fMRI imaging data in this population with cognitively normal (CN) and mild Alzheimer's disease (AD) subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI)-2/3 database.

Omniscient, a for-profit, Sydney, Australia-based company, created the cloud-based software Infinitome, a program that utilizes data from the Human Connectome Project (HCP) together with machine learning to analyze diffusion tensor and resting-state fMRI imaging data from remote sites. The foundation for this imaging tool is based upon the HCP atlas, which has also informed prior publications from our group, including the Connectomic Atlas of the Human Cerebrum (Baker, Burks, Briggs, Conner, et al. 2018). The Infinitome program creates a subject specific version of the Human Connectome Project Multimodal Parcellation (HCP-MMP1) atlas using diffusion tractography. Analytics are performed on both diffusion tensor imaging and rs-fMRI. Outlier detection using a tangent space connectivity matrix is performed by comparing results with a subset of 300 normal HCP subject fMRI samples to determine the range of normal correlations for each regions of interest in a large scale brain network which include:

1. Locus coeruleus vs. Nucleus basalis of Meynert
2. Locus coeruelus vs. Intralaminar nucleus of the thalamus
3. Nucleus basalis of Meynert vs. Intralaminar nuclei of the thalamus
4. FST in the vs. Area PH in the lateral occipital lobe
5. Substantia nigra vs. Caudate nucleus Clinical Measures will be correlated with functional connectivity scores to identify relationship between clinical symptoms and large scale brain networks in DLB

Inclusion Criteria:

• Age 40-90 years
• Established DLB diagnosis
• Mini mental status exam (MMSE) >15

Exclusion Criteria:

• Other forms of dementia including, but not limited to Alzheimer's dementia, frontotemporal dementia, vascular dementia, normal pressure hydrocephalus (NPH), etc,
• Inability to tolerate brain function magnetic resonance imaging (fMRI)
• Risk of brain fMRI due to implants or metal.

HealthPartners Neurology Clinic in Saint Paul, MN undergoing care for DLB and subjects included in the ADNI database.

2 months for DLB patients, No adverse event data collected for AD or CN patients.

Adverse event data was not collected for AD and CN patients - study data was from ADNI-2/3 database.

DLB patients (n=18) were matched 2:1 to two control groups taken from the ADNI database: older adults with AD/CI confirmed by biomarkers and/or PET testing, and older adults who were cognitively normal with negative results from biomarker and/or PET testing. Both groups were matched by age, but only the AD/CI group was matched by MMSE score. Matching by sex was not possible due to the high prevalence of men in the DLB sample (83%) vs ADNI (46% male).