Automatic classification of cortical thickness patterns in Alzheimer's disease patients, using the Louvain modularity clustering method

Alzheimer's disease is heterogeneous and despite some consistent neuropathological hallmarks, different clinical forms have been identified, including non-amnestic presentations. Even in anmestic forms, the presentation of the disease can differ across individuals, in terms of age of onset, dynamics of progression and specific impairment profiles. Different distributions of neurofibrillary tangles exist in AD, and these are linked with structural differences detectable on ante-mortem MRI. but these are hard to identify in the earlier stages of disease. In the present work, we validate and test a previously proposed method for identifying subtypes of cortical atrophy in AD, based on MRI data from an independent case/control study of individuals defined by pathophysiological biomarkers. We implemented a clustering method based on the Louvain modularity method, and tested it across a range of pre-processing parameters. Our cohort of participants was comprised of 111 participants (mean age: 67.7 year; range: 51-91), including 37 cognitively normal controls, 43 prodromal AD, and 31 demented AD patients. We identified 4 patient clusters with distinct atrophy patterns either predominantly in the temporal lobes (groups 0 and 1), in the parietal and temporal lobes (group 2), or in the frontal and temporal lobes (group 3). Further evaluation of neuro-psychological characteristics of each patient cluster will be carried out in the future. In conclusion, the modularity-based clustering method may help to identify specific subtypes of atrophy in neurological diseases such as AD.