Statistical inference in brain graphs using threshold-free network-based statistics.

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.

Nigral and striatal connectivity alterations in asymptomatic LRRK2 mutation carriers: A magnetic resonance imaging study.

BACKGROUND: The study of functional connectivity by means of magnetic resonance imaging (MRI) in asymptomatic LRRK2 mutation carriers could contribute to the characterization of the prediagnostic phase of LRRK2-associated Parkinson's disease (PD). The objective of this study was to characterize MRI functional patterns during the resting state in asymptomatic LRRK2 mutation carriers. METHODS: We acquired structural and functional MRI data of 18 asymptomatic LRRK2 mutation carriers and 18 asymptomatic LRRK2 mutation noncarriers, all first-degree relatives of LRRK2-PD patients. Starting from resting-state data, we analyzed the functional connectivity of the striatocortical and the nigrocortical circuitry. Structural brain data were analyzed by voxel-based morphometry, cortical thickness, and volumetric measures. RESULTS: Asymptomatic LRRK2 mutation carriers had functional connectivity reductions between the caudal motor part of the left striatum and the ipsilateral precuneus and superior parietal lobe. Connectivity in these regions correlated with subcortical gray-matter volumes in mutation carriers. Asymptomatic carriers also showed increased connectivity between the right substantia nigra and bilateral occipital cortical regions (occipital pole and cuneus bilaterally and right lateral occipital cortex). No intergroup differences in structural MRI measures were found. In LRRK2 mutation carriers, age and functional connectivity correlated negatively with striatal volumes. Additional analyses including only subjects with the G2019S mutation revealed similar findings. CONCLUSIONS: Asymptomatic LRRK2 mutation carriers showed functional connectivity changes in striatocortical and nigrocortical circuits compared with noncarriers. These findings support the concept that altered brain connectivity precedes the onset of classical motor features in a genetic form of PD. © 2016 International Parkinson and Movement Disorder Society.

Rich Club Organization and Cognitive Performance in Healthy Older Participants.

The human brain is a complex network that has been noted to contain a group of densely interconnected hub regions. With a putative "rich club" of hubs hypothesized to play a central role in global integrative brain functioning, we assessed whether hub and rich club organizations are associated with cognitive performance in healthy participants and whether the rich club might be differentially involved in cognitive functions with a heavier dependence on global integration. A group of 30 relatively older participants (range = 39-79 years of age) underwent extensive neuropsychological testing, combined with diffusion-weighted magnetic resonance imaging to reconstruct individual structural brain networks. Rich club connectivity was found to be associated with general cognitive performance. More specifically, assessing the relationship between the rich club and performance in two specific cognitive domains, we found rich club connectivity to be differentially associated with attention/executive functions-known to rely on the integration of distributed brain areas-rather than with visuospatial/visuoperceptual functions, which have a more constrained neuroanatomical substrate. Our findings thus provide first empirical evidence of a relevant role played by the rich club in cognitive processes.

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.

Functional MRI in Parkinson's Disease Cognitive Impairment.

Functional magnetic resonance imaging (fMRI) has been used to study the neural bases of cognitive deficits in Parkinson's disease for several years. Traditionally, task-based fMRI has been applied to study specific cognitive functions, providing information on disease-related alterations and regarding the physiological bases of normal cognition, the dopaminergic system, and the frontostriatal circuits. More recently, functional connectivity techniques using resting-state fMRI data have been developed. Unconstrained by specific cognitive tasks, these techniques allow assessing whole-brain patterns of connectivity believed to be useful proxies for the underlying functional architecture of the brain. These methods have shown that different types of Parkinson's disease-related cognitive deficits are associated with patterns of altered connectivity within and between resting-state intrinsic connectivity networks. Although methodological standardization and the vulnerability of fMRI techniques to artifacts mandate further technical refinement, early studies provide encouraging results regarding the potential of fMRI-derived parameters for the ultimate goal of individual-subject classification.

Neuroanatomical and Functional Correlates of Cognitive and Affective Empathy in Young Healthy Adults.

Neural substrates of empathy are mainly investigated through task-related functional MRI. However, the functional neural mechanisms at rest underlying the empathic response have been poorly studied. We aimed to investigate neuroanatomical and functional substrates of cognitive and affective empathy. The self-reported empathy questionnaire Cognitive and Affective Empathy Test (TECA), T1 and T2∗-weighted 3-Tesla MRI were obtained from 22 healthy young females (mean age: 19.6 ± 2.4) and 20 males (mean age: 22.5 ± 4.4). Groups of low and high empathy were established for each scale. FreeSurfer v6.0 was used to estimate cortical thickness and to automatically segment the subcortical structures. FSL v5.0.10 was used to compare resting-state connectivity differences between empathy groups in six defined regions: the orbitofrontal, cingulate, and insular cortices, and the amygdala, hippocampus, and thalamus using a non-parametric permutation approach. The high empathy group in the Perspective Taking subscale (cognitive empathy) had greater thickness in the left orbitofrontal and ventrolateral frontal cortices, bilateral anterior cingulate, superior frontal, and occipital regions. Within the affective empathy scales, subjects with high Empathic Distress had higher thalamic volumes than the low-empathy group. Regarding resting-state connectivity analyses, low-empathy individuals in the Empathic Happiness scale had increased connectivity between the orbitofrontal cortex and the anterior cingulate when compared with the high-empathy group. In conclusion, from a structural point of view, there is a clear dissociation between the brain correlates of affective and cognitive factors of empathy. Neocortical correlates were found for the cognitive empathy dimension, whereas affective empathy is related to lower volumes in subcortical structures. Functionally, affective empathy is linked to connectivity between the orbital and cingulate cortices.

Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography.

Recent studies combining diffusion tensor-derived metrics and machine learning have shown promising results in the discrimination of multiple system atrophy (MSA) and Parkinson's disease (PD) patients. This approach has not been tested using more complex methodologies such as probabilistic tractography. The aim of this work is assessing whether the strength of structural connectivity between subcortical structures, measured as the number of streamlines (NOS) derived from tractography, can be used to classify MSA and PD patients at the single-patient level. The classification performance of subcortical FA and MD was also evaluated to compare the discriminant ability between diffusion tensor-derived metrics and NOS. Using diffusion-weighted images acquired in a 3 T MRI scanner and probabilistic tractography, we reconstructed the white matter tracts between 18 subcortical structures from a sample of 54 healthy controls, 31 MSA patients and 65 PD patients. NOS between subcortical structures were compared between groups and entered as features into a machine learning algorithm. Reduced NOS in MSA compared with controls and PD were found in connections between the putamen, pallidum, ventral diencephalon, thalamus, and cerebellum, in both right and left hemispheres. The classification procedure achieved an overall accuracy of 78%, with 71% of the MSA subjects and 86% of the PD patients correctly classified. NOS features outperformed the discrimination performance obtained with FA and MD. Our findings suggest that structural connectivity derived from tractography has the potential to correctly distinguish between MSA and PD patients. Furthermore, NOS measures obtained from tractography might be more useful than diffusion tensor-derived metrics for the detection of MSA.

Cortical Gray Matter and Hippocampal Atrophy in Idiopathic Rapid Eye Movement Sleep Behavior Disorder.

Objective: In this study we investigate cortical and subcortical gray matter structure in patients with Idiopathic REM-sleep behavior disorder (IRBD), and their relation to cognitive performance. Methods: This study includes a sample of 20 patients with polysomnography-confirmed IRBD and 27 healthy controls that underwent neuropsychological and T1-weighted MRI assessment. FreeSurfer was used to estimate cortical thickness, subcortical volumetry (version 5.1), and hippocampal subfields segmentation (version 6.0). FIRST, FSL's model-based segmentation/registration tool was used for hippocampal shape analysis. Results: Compared with healthy subjects, IRBD patients showed impairment in facial recognition, verbal memory, processing speed, attention, and verbal naming. IRBD patients had cortical thinning in left superior parietal, post-central, and fusiform regions, as well as in right superior frontal and lateral occipital regions. Volumetric and shape analyses found right hippocampal atrophy in IRBD, specifically in posterior regions. Hippocampal subfields exploratory analysis identified significant differences in the right CA1, molecular layer, granule cell layer of dentate gyrus, and CA4 of this patients. No correlations were found between cognitive performance and brain atrophy. Conclusion: This work confirms the presence of posterior based cognitive dysfunction, as well as cortical and right hippocampal atrophy in IRBD patients.

Cerebellar resting-state functional connectivity in Parkinson's disease and multiple system atrophy: Characterization of abnormalities and potential for differential diagnosis at the single-patient level.

BACKGROUND: Recent studies using resting-state functional connectivity and machine-learning to distinguish patients with neurodegenerative diseases from other groups of subjects show promising results. This approach has not been tested to discriminate between Parkinson's disease (PD) and multiple system atrophy (MSA) patients. OBJECTIVES: Our first aim is to characterize possible abnormalities in resting-state functional connectivity between the cerebellum and a set of intrinsic-connectivity brain networks and between the cerebellum and different regions of the striatum in PD and MSA. The second objective of this study is to assess the potential of cerebellar connectivity measures to distinguish between PD and MSA patients at the single-patient level. METHODS: Fifty-nine healthy controls, 62 PD patients, and 30 MSA patients underwent resting-state functional MRI with a 3T scanner. Independent component analysis and dual regression were used to define seven resting-state networks of interest. To assess striatal connectivity, a seed-to-voxel approach was used after dividing the striatum into six regions bilaterally. Measures of cerebellar-brain network and cerebellar-striatal connectivity were then used as features in a support vector machine to discriminate between PD and MSA patients. RESULTS: MSA patients displayed reduced cerebellar connectivity with different brain networks and with the striatum compared with PD patients and with controls. The classification procedure achieved an overall accuracy of 77.17% with 83.33% of the MSA subjects and 74.19% of the PD patients correctly classified. CONCLUSION: Our findings suggest that measures of cerebellar functional connectivity have the potential to distinguish between PD and MSA patients.

Disrupted structural connectivity of fronto-deep gray matter pathways in progressive supranuclear palsy.

BACKGROUND: Structural connectivity is a promising methodology to detect patterns of neural network dysfunction in neurodegenerative diseases. This approach has not been tested in progressive supranuclear palsy (PSP). OBJECTIVES: The aim of this study is reconstructing the structural connectome to characterize and detect the pathways of degeneration in PSP patients compared with healthy controls and their correlation with clinical features. The second objective is to assess the potential of structural connectivity measures to distinguish between PSP patients and healthy controls at the single-subject level. METHODS: Twenty healthy controls and 19 PSP patients underwent diffusion-weighted MRI with a 3T scanner. Structural connectivity, represented by number of streamlines, was derived from probabilistic tractography. Global and local network metrics were calculated based on graph theory. RESULTS: Reduced numbers of streamlines were predominantly found in connections between frontal areas and deep gray matter (DGM) structures in PSP compared with controls. Significant changes in structural connectivity correlated with clinical features in PSP patients. An abnormal small-world architecture was detected in the subnetwork comprising the frontal lobe and DGM structures in PSP patients. The classification procedure achieved an overall accuracy of 82.23% with 94.74% sensitivity and 70% specificity. CONCLUSION: Our findings suggest that modelling the brain as a structural connectome is a useful method to detect changes in the organization and topology of white matter tracts in PSP patients. Secondly, measures of structural connectivity have the potential to correctly discriminate between PSP patients and healthy controls.

Differential Progression of Regional Hippocampal Atrophy in Aging and Parkinson's Disease.

Hippocampal subfields have different vulnerability to the degenerative processes related to aging, amnestic mild cognitive impairment (MCI) and Alzheimer's disease (AD), but the temporal evolution in Parkinson's disease (PD) is unknown. The purposes of the current work are to describe regional hippocampal changes over time in a sample of PD patients classified according to their baseline cognitive status and to relate these changes to verbal memory loss. T1-weighted images and verbal memory assessment were obtained at two separate time points (3.8 ± 0.4 years apart) from 28 PD with normal cognition (PD-NC), 16 PD with MCI (PD-MCI) and 21 healthy controls (HCs). FreeSurfer 6.0 automated pipeline was used to segment the hippocampus into 12 bilateral subregions. Memory functions were measured with Rey's Auditory Verbal learning test (RAVLT). We found significant reductions in cornu ammonis 1 (CA1) over time in controls as well as in PD subgroups. Right whole-hippocampal volumes showed time effects in both PD groups but not in controls. PD-NC patients also displayed time effects in the left hippocampal tail and right parasubiculum. Regression analyses showed that specific hippocampal subfield volumes at time 1 predicted almost 60% of the variability in RAVLT delayed-recall score decline. Changes in several hippocampal subregions also showed predictive value for memory loss. In conclusion, CA1 changes in PD were similar to those that occur in normal aging, but PD patients also had more decline in both anterior and posterior hippocampal segments with a more pronounced atrophy of the right hemisphere. Hippocampal segments are better predictors of changes in memory performance than whole-hippocampal volumes.

Gray/White Matter Contrast in Parkinson's Disease.

Gray/white matter contrast (GWC) decreases with aging and has been found to be a useful MRI biomarker in Alzheimer's disease (AD), but its utility in Parkinson's disease (PD) patients has not been investigated. The aims of the study were to test whether GWC is sensitive to aging changes in PD patients, if PD patients differ from healthy controls (HCs) in GWC, and whether the use of GWC data would improve the sensitivity of cortical thickness analyses to differentiate PD patients from controls. Using T1-weighted structural images, we obtained individual cortical thickness and GWC values from a sample of 90 PD patients and 27 controls. Images were processed with the automated FreeSurfer stream. GWC was computed by dividing the white matter (WM) by the gray matter (GM) values and projecting the ratios onto a common surface. The sample characteristics were: 52 patients and 14 controls were males; mean age of 64.4 ± 10.6 years in PD and 64.7 ± 8.6 years in controls; 8.0 ± 5.6 years of disease evolution; 15.6 ± 9.8 UPDRS; and a range of 1.5-3 in Hoehn and Yahr (H&Y) stage. In both PD and controls we observed significant correlations between GWC and age involving almost the entire cortex. When applying a stringent cluster-forming threshold of p < 0.0001, the correlation between GWC and age also involved the entire cortex in the PD group; in the control group, the correlation was found in the parahippocampal gyrus and widespread frontal and parietal areas. The GWC of PD patients did not differ from controls', whereas cortical thickness analyses showed thinning in temporal and parietal cortices in the PD group. Cortical thinning remained unchanged after adjusting for GWC. GWC is a very sensitive measure for detecting aging effects, but did not provide additional information over other parameters of atrophy in PD.

Brain correlates of progressive olfactory loss in Parkinson's disease.

BACKGROUND: Olfactory dysfunction is present in a large proportion of patients with Parkinson's disease (PD) upon diagnosis. However, its progression over time has been poorly investigated. The few available longitudinal studies lack control groups or MRI data. OBJECTIVE: To investigate the olfactory changes and their structural correlates in non-demented PD over a four-year follow-up. METHODS: We assessed olfactory function in a sample of 25 PD patients and 24 normal controls of similar age using the University of Pennsylvania Smell Identification test (UPSIT). Structural magnetic resonance imaging data, obtained with a 3-T Siemens Trio scanner, were analyzed using FreeSurfer software. RESULTS: Analysis of variance showed significant group (F = 53.882; P < 0.001) and time (F = 6.203; P = 0.016) effects, but the group-by-time interaction was not statistically significant. UPSIT performance declined ≥1.5 standard deviations in 5 controls and 7 patients. Change in UPSIT scores of patients correlated positively with volume change in the left putamen, right thalamus, and right caudate nucleus. CONCLUSION: Olfactory loss over time in PD and controls is similar, but we have observed significant correlation between this loss and basal ganglia volumes only in patients.

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.

Resting-state functional brain networks 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.

Aspectos relevantes da interface entre diabetes mellitus e infecção / Relevant aspect in the interface between diabetes mellitus and infection

O diabetes mellitus (DM) é uma doença de alta prevalência nas sociedades modernas, na maioria das vezes com tratamento inadequado ou ausente. Apesar de geralmente considerado como fator de risco independente para ocorrência e gravidade de infecções em geral, o DM não apresenta evidência clínica forte de sua relação com infecção. Observa-se, porém, uma maior ocorrência de certas infecções em pacientes com DM, com curso menos favorável para algumas delas. Há também tipos de infecção quase exclusivos de pacientes com DM. Experimentalmente, observa-se depressão da atividade dos neutrófilos, menor eficiência da imunidade celular, alteração dos sistemas antìoxidantes e menor produção de interleucinas. Com relação às infecções comuns, as que envolvem o trato respiratório não têm comprovadamente maior gravidade em pacientes com DM, exceção feita ao pneumococo - por isso a recomendação para sua vacinação contra S. pneumoniae e influenza. Quanto ao trato urinário, há maior ocorrência de bacteriúria assintomática em mulheres com DM, com maiores índices de pielonefrite, necrose papilar, abscesso perinéfrico, pìelonefrite xantogranulomatosa, e cistite e pielonefrite gangrenosas. Periodontite e infecções de partes moles são também mais comuns no DM. Cada tipo de infecção é associado a germes típicos, e seu conhecimento é fundamental para um tratamento inicial adequado. As infecções quase exclusivas de pacientes com DM incluem otite externa maligna, mucormicose rinocerebral, colecistìte gangrenosa e o somatório de alterações que caracterizam o pé diabético. O conhecimento destas infecções assume maior importância por requererem freqüentemente uma abordagem multidisciplinar, envolvendo endocrinologistas, infectologistas, cirurgiões vasculares e nefrologistas, dentre outros.