The aim of this study was to investigate patterns of cortical atrophy associated with mild cognitive impairment in a large sample of nondemented Parkinson's disease (PD) patients, and its relation with specific neuropsychological deficits. Magnetic resonance imaging (MRI) and neuropsychological assessment were performed in a sample of 90 nondemented PD patients and 32 healthy controls. All underwent a neuropsychological battery including tests that assess different cognitive domains: attention and working memory, executive functions, memory, language, and visuoperceptual-visuospatial functions. Patients were classified according to their cognitive status as PD patients without mild cognitive impairment (MCI; n = 43) and PD patients with MCI (n = 47). Freesurfer software was used to obtain maps of cortical thickness for group comparisons and correlation with neuropsychological performance. Patients with MCI showed regional cortical thinning in parietotemporal regions, increased global atrophy (global cortical thinning, total gray matter volume reduction, and ventricular enlargement), as well as significant cognitive impairment in memory, executive, and visuospatial and visuoperceptual domains. Correlation analyses showed that all neuropsychological tests were associated with cortical thinning in parietotemporal regions and to a lesser extent in frontal regions. These results provide neuroanatomic support to the concept of MCI classified according to Movement Disorders Society criteria. The posterior pattern of atrophy in temporoparietal regions could be a structural neuroimaging marker of cognitive impairment in nondemented PD patients. All of the neuropsychological tests reflected regional brain atrophy, but no specific patterns were seen corresponding to impairment in distinct cognitive domains.

The description of brain networks as graphs where nodes represent different brain regions and edges represent a measure of connectivity between a pair of nodes is an increasingly used approach in neuroimaging research. The development of powerful methods for edge-wise group-level statistical inference in brain graphs while controlling for multiple-testing associated false-positive rates, however, remains a difficult task. In this study, we use simulated data to assess the properties of threshold-free network-based statistics (TFNBS). The TFNBS combines threshold-free cluster enhancement, a method commonly used in voxel-wise statistical inference, and network-based statistic (NBS), which is frequently used for statistical analysis of brain graphs. Unlike the NBS, TFNBS generates edge-wise significance values and does not require the a priori definition of a hard cluster-defining threshold. Other test parameters, nonetheless, need to be set. We show that it is possible to find parameters that make TFNBS sensitive to strong and topologically clustered effects, while appropriately controlling false-positive rates. Our results show that the TFNBS is an adequate technique for the statistical assessment of brain graphs.

There is growing interest in the potential of neuroimaging to help develop non-invasive biomarkers in neurodegenerative diseases. In this study, connection-wise patterns of functional connectivity were used to distinguish Parkinson’s disease patients according to cognitive status using machine learning. Two independent subject samples were assessed with resting-state fMRI. The first (training) sample comprised 38 healthy controls and 70 Parkinson’s disease patients (27 with mild cognitive impairment). The second (validation) sample included 25 patients (8 with mild cognitive impairment). The Brainnetome atlas was used to reconstruct the functional connectomes. Using a support vector machine trained on features selected through randomized logistic regression with leave-one-out cross-validation, a mean accuracy of 82.6% (p < 0.002) was achieved in separating patients with mild cognitive impairment from those without it in the training sample. The model trained on the whole training sample achieved an accuracy of 80.0% when used to classify the validation sample (p = 0.006). Correlation analyses showed that the connectivity level in the edges most consistently selected as features was associated with memory and executive function performance in the patient group. Our results demonstrate that connection-wise patterns of functional connectivity may be useful for discriminating Parkinson’s disease patients according to the presence of cognitive deficits.

Even in the early stages of PD, there is evidence of cortical brain atrophy. Neuroimaging clustering analysis is able to detect two subgroups of cortical thinning, one with mainly anterior atrophy, and the other with posterior predominance and worse cognitive performance.

In this study, we establish the neuroanatomical substrate of progressive changes in VS/VP performance in PD patients with and without MCI. In agreement with cross-sectional data, VS/VP changes over time are related to cortical thinning in posterior regions.