Resting-state functional brain netwoks in Parkinson's disease

The network approach is increasingly being applied to the investigation of normal brain function and its impairment. In the present review, we introduce the main methodological approaches employed for the analysis of resting-state neuroimaging data in Parkinson's disease studies. We then summarize the results of recent studies that used a functional network perspective to evaluate the changes underlying different manifestations of Parkinson's disease, with an emphasis on its cognitive symptoms. Despite the variability reported by many studies, these methods show promise as tools for shedding light on the pathophysiological substrates of different aspects of Parkinson's disease, as well as for differential diagnosis, treatment monitoring and establishment of imaging biomarkers for more severe clinical outcomes

Visuospatial and visuoperceptual impairment in relation to global atrophy in Parkinson's diseas

Parkinson's disease (PD) patients differed from controls of similar age in visuospatial and visuoperceptual functions at diagnosis moment, and these deficits have been shown to be neuropsychological markers of evolution to dementia. The aim of this study was to relate these dysfunctions with measures of brain. The sample of this study consisted of 92 PD patients and 36 healthy subjects matched by age, sex and education. All subjects were evaluated with Judgment of Line Orientation, Visual Form Discrimination and Facial Recognition Tests and magnetic resonance imaging at 3 Tesla. We found significant differences between patients and controls in all three tests and in the mean of cortical thickness, gray matter volume and ventricular system. All visuospatial and visuoperceptual tests correlated with the measures of global atrophy suggesting that they are reflecting the brain degeneration associated to PD

Neuroanatomical correlates of olfactory loss in normal aged subjects

In non-demented older persons, smell dysfunction, measured premortem, has been associated with postmortem brain degeneration similar to that of Alzheimer's disease. We hypothesized that distinct measures of gray and white matter integrity evaluated through magnetic resonance imaging (MRI) techniques could detect degenerative changes associated with age-related olfactory dysfunction. High-resolution T1-weighted images and diffusion-tensor images (DTI) of 30 clinically healthy subjects aged 51 to 77 were acquired with a 3-Tesla MRI scanner. Odor identification performance was assessed by means of the University of Pennsylvania Smell Identification Test (UPSIT). UPSIT scores correlated with right amygdalar volume and bilateral perirhinal and entorhinal cortices gray matter volume. Olfactory performance also correlated with postcentral gyrus cortical thickness and with fractional anisotropy and mean diffusivity levels in the splenium of the corpus callosum and the superior longitudinal fasciculi. Our results suggest that age-related olfactory loss is accompanied by diffuse degenerative changes that might correspond to the preclinical stages of neurodegenerative processes

Discriminating cognitive status in Parkinson's disease through functional connectomics and machine learning

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

Functional brain networks and cognitive deficits in Parkinson's disease

Abstract: Graph-theoretical analyses of functional networks obtained with resting-state functional mag-netic resonance imaging (fMRI) have recently proven to be a useful approach for the study of the sub-strates underlying cognitive deficits in different diseases. We used this technique to investigate whethercognitive deficits in Parkinson's disease (PD) are associated with changes in global and local networkmeasures. Thirty-six healthy controls (HC) and 66 PD patients matched for age, sex, and education wereclassified as having mild cognitive impairment (MCI) or not based on performance in the three mainlyaffected cognitive domains in PD: attention/executive, visuospatial/visuoperceptual (VS/VP), anddeclarative memory. Resting-state fMRI and graph theory analyses were used to evaluate network meas-ures. We have found that patients with MCI had connectivity reductions predominantly affecting long-range connections as well as increased local interconnectedness manifested as higher measures of cluster-ing, small-worldness, and modularity. The latter measures also tended to correlate negatively with cogni-tive performance in VS/VP and memory functions. Hub structure was also reorganized: normal hubsdisplayed reduced centrality and degree in MCI PD patients. Our study indicates that the topologicalproperties of brain networks are changed in PD patients with cognitive deficits. Our findings providenovel data regarding the functional substrate of cognitive impairment in PD, which may prove to havevalue as a prognostic marker

Structural correlates of facial emotion recognition deficits in Parkinson's disease patients

The ability to recognize facial emotion expressions, especially negative ones, is described to be impaired in Parkinson's disease (PD) patients. Previous neuroimaging work evaluating the neural substrate of facial emotion recognition (FER) in healthy and pathological subjects has mostly focused on functional changes. This study was designed to evaluate gray matter (GM) and white matter (WM) correlates of FER in a large sample of PD. Thirty-nine PD patients and 23 healthy controls (HC) were tested with the Ekman 60 test for FER and with magnetic resonance imaging. Effects of associated depressive symptoms were taken into account. In accordance with previous studies, PD patients performed significantly worse in recognizing sadness, anger and disgust. In PD patients, voxel-based morphometry analysis revealed areas of positive correlation between individual emotion recognition and GM volume: in the right orbitofrontal cortex, amygdala and postcentral gyrus and sadness identification; in the right occipital fusiform gyrus, ventral striatum and subgenual cortex and anger identification, and in the anterior cingulate cortex (ACC) and disgust identification. WM analysis through diffusion tensor imaging revealed significant positive correlations between fractional anisotropy levels in the frontal portion of the right inferior fronto-occipital fasciculus and the performance in the identification of sadness. These findings shed light on the structural neural bases of the deficits presented by PD patients in this skill

Resting-state frontostriatal functional connectivity in Parkinson's disease-related apathy

Background: One of the most common neuropsychiatric symptoms in PD is apathy, affecting between 23 and 70% of patients and thought to be related to frontostriatal dopamine deficits. In the present study, we assessed functional resting-state frontostriatal connectivity and structural changes associated with the presence of apathy in a large sample of PD subjects and healthy controls, while controlling for the presence of comorbid depression and cognitive decline. Methods: Thirty-one healthy controls (HC) and 62 age, sex and education-matched PD patients underwent resting-state functional MRI. Apathy symptoms were evaluated with the Apathy Scale (AS). The 11 Beck Depression Inventory-II items that measure dysphoric mood symptoms as well as relevant neuropsychological scores were used as nuisance factors in connectivity analyses. Voxel-wise analyses of functional connectivity between frontal lobes (limbic, executive, rostral motor and caudal motor regions), striata (limbic, executive, sensorimotor regions) and thalami were performed. Subcortical volumetry/shape analysis and fronto-subcortical voxel-based morphometry were performed to assess structural changes. Results: Twenty-five PD patients were classified as apathetic (PD-A) (AS>13). PD-A patients showed functional connectivity reductions compared with HC and with non-apathetic patients (PD-NA), mainly in left-sided circuits, and predominantly involving limbic striatal and frontal territories. Similarly, severity of apathy negatively correlated with connectivity in these circuits. No significant effects were found in structural analyses. Conclusions: Our results indicate that the presence of apathy in PD is associated with functional connectivity reductions in frontostriatal circuits, predominating in the left hemisphere and mainly involving its limbic components

Cognitive impairment and resting-state network connectivity in Parkinson's disease

Previous functional MRI studies have revealed changes in the default-mode network (DMN) in Parkinson's disease (PD). The purpose of this work was to evaluate changes in the connectivity patterns of a set of cognitively relevant, dynamically interrelated brain networks in association with cognitive deficits in PD using resting-state functional MRI. Sixty-five non-demented PD patients and 36 matched healthy controls (HC) were included. Thirty-four percent of PD patients were classified as having mild cognitive impairment (MCI) based on performance in the three mainly-affected cognitive domains in Parkinson's disease (attention/executive, visuospatial/visuoperceptual and declarative memory). Data-driven analyses through independent-component analysis (ICA) was used to identify the DMN, the dorsal attention network (DAN) and the bilateral frontoparietal networks (FPN), which were compared between groups using a dual-regression approach. Additional seed-based analyses using a-priori defined regions of interest were used to characterize local changes in intra and inter-network connectivity. ICA results revealed reduced connectivity between the DAN and right frontoinsular cortical regions in MCI patients, which correlated with worse performance in attention/executive functions. The DMN, on the other hand, displayed increased connectivity with medial and lateral occipito-parietal regions in MCI patients; these increases correlated with worse visuospatial/visuoperceptual performance. In line with data-driven results, seed-based analyses mainly revealed reduced within-DAN, within-DMN and DAN-FPN connectivity, as well as increased DAN-DMN coupling in MCI patients. Our findings demonstrate differential connectivity changes affecting the networks evaluated, which we hypothesize to be related to the pathophysiological bases of different types of cognitive impairment in PD

Progression of cortical thinning in early Parkinson's disease

The aim of this study was to investigate the progression of cortical thinning and gray-matter (GM) volume loss in early Parkinson's disease (PD). MRI and neuropsychological assessment were obtained at baseline and follow-up (mean ± standard deviation = 35.50 ± 1.88 months) in a group of 16 early-PD patients (H & Y stage ≤II and disease duration ≤5 years) and 15 healthy controls matched for age, gender, and years of education. FreeSurfer software was used for the analysis of cortical thickness as well as for cortical and subcortical volumetric analyses. Voxel-based morphometry analysis was performed using SPM8. Compared to controls, PD patients showed greater regional cortical thinning in bilateral frontotemporal regions as well as greater over-time total GM loss and amygdalar volume reduction. PD patients and controls presented similar over-time changes in cognitive functioning. In early-PD patients, global GM loss, amygdalar atrophy, and cortical thinning in frontotemporal regions are specifically associated with the PD-degenerative process

Patterns of cortical thinning in nondemented Parkinson's disease patients

Background: Clinical variability in the Parkinson's disease phenotype suggests the existence of disease subtypes. We investigated whether distinct anatomical patterns of atrophy can be identified in Parkinson's disease using a hypothesis-free, datadriven approach based on cortical thickness data. Methods: T1-weighted 3-tesla MRI and a comprehensive neuropsychological assessment were performed in a sample of 88 nondemented Parkinson's disease patients and 31 healthy controls. We performed a hierarchical cluster analysis of imaging data using Ward's linkage method. A general linear model with cortical thickness data was used to compare clustering groups. Results: We observed 3 patterns of cortical thinning in patients when compared with healthy controls. Pattern 1 (n530, 34.09%) consisted of cortical atrophy in bilateral precentral gyrus, inferior and superior parietal lobules, cuneus, posterior cingulate, and parahippocampal gyrus. These patients showed worse cognitive performance when compared with controls and the other 2 patterns. Pattern 2 (n529, 32.95%) consisted of cortical atrophy involving occipital and frontal as well as superior parietal areas and included patients with younger age at onset. Finally, in pattern 3 (n529, 32.95%), there was no detectable cortical thinning. Patients in the 3 patterns did not differ in disease duration, motor severity, dopaminergic medication doses, or presence of mild cognitive impairment. Conclusions: Three cortical atrophy subtypes were identified in nondemented Parkinson's disease patients: (1) parieto-temporal pattern of atrophy with worse cognitive performance, (2) occipital and frontal cortical atrophy and younger disease onset, and (3) patients without detectable cortical atrophy. These findings may help identify prognosis markers in Parkinson's disease. VC 2016 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Societ