Alzheimer’s disease is heterogeneous and despite some consistent neuropathological hallmarks, different clinical forms have been identified, including non-amnestic presentations. Even in amnestic 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.
Parkinson's disease (PD) is a progressive neurodegenerative disorder in which patients show progressively worsening motor symptoms, often followed by cognitive impairment and dementia. Brain MRI can be used to identify patterns of neurodegeneration that are characteristic of PD, but the spatial pattern of brain abnormalities is still not well understood. “Sulcus-based morphometry” provides measures of the cortical fissures of the brain that reflect degenerative changes in relation to neuropsychiatric disease. Extracting sulci requires good contrast between the gray matter and the CSF, and less well-defined sulci may be difficult to extract reliably. Before embarking on a study of sulcal abnormalities in PD, we set out to determine the reliability of measures from 123 sulci, defined by an existing atlas, using publicly available test-retest data from 8 cohorts. Of the 123 atlas-defined sulci, several major sulci were broken down into smaller regions (e.g., the superior temporal sulcus was divided into the main STS, the anterior terminal ascending branch of STS and the posterior terminal ascending branch of STS); we assessed reliability in each individually, and after merging the portions of the sulci together, in a newly defined, concatenated atlas. For 467 subjects from the PPMI cohort (http://www.ppmiinfo. org ;age range: 61.5 ± 10.1 years), we segmented and labeled major sulci and extracted 4 shape descriptors for each: length, depth, surface area, and width. We then aimed to establish the profile of case-control differences for 3 candidate sulci of interest: the central sulcus, superior temporal sulcus and the calcarine fissure. These sulci were among the more robust in terms of reproducibility; we found that the calcarine width was associated with PD, offering new features for genetic and interventional studies of PD.
We present an algorithm to align cortical surface models based on structural connectivity. We follow the continuous connectivity approach,<sup>1, 2</sup> assigning a dense connectivity to every surface point-pair. We adapt and modify an approach for aligning low-rank functional networks based on eigenvalue decomposition of individual connectomes.<sup>3</sup> The spherical demons framework then provides a natural setting for inter-subject connectivity alignment, enforcing a smooth, anatomically plausible correspondence, and allowing us to incorporate anatomical as well as connectivity information. We apply our algorithm to 98 diffusion MRI images in an Alzheimer's Disease study, and 731 healthy subjects from the Human Connectome Project. Our method consistently reduces connectome variability due to misalignment. Further, the approach reveals subtle disease effects on structural connectivity which are not seen when registering only cortical anatomy.
In the present work we study a family of generative network model and its applications for modeling the human connectome. We introduce a minor but novel variant of the Mixed Membership Stochastic Blockmodel and apply it and two other related model to two human connectome datasets (ADNI and a Bipolar Disorder dataset) with both control and diseased subjects. We further provide a simple generative classifier that, alongside more discriminating methods, provides evidence that blockmodels accurately summarize tractography count networks with respect to a disease classification task.