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

Structural and functional magnetic resonance imaging in isolated REM sleep behavior disorder: A systematic review of studies using neuroimaging software.

Isolated rapid eye movement sleep behavior disorder (iRBD) is a harbinger for developing clinical synucleinopathies. Magnetic resonance imaging (MRI) has been suggested as a tool for understanding the brain bases of iRBD and its evolution. This review systematically analyzed original full text articles on structural and functional MRI in patients with video-polysomnography-confirmed iRBD according to systematic procedures suggested by Reviews and Meta-analyses (PRISMA). The literature search was conducted via the PubMed database for articles related to structural and functional MRI in iRBD from 2000 to 2020. Investigations to date have been diverse in terms of methodology, but most agree that patients with iRBD have structural changes in deep gray matter nuclei, cortical gray matter atrophy, and disrupted functional connectivity within the basal ganglia, the cortico-striatal and cortico-cortical networks. Furthermore, there is evidence that MRI detects structural and functional brain changes associated with the motor and non-motor symptoms of iRBD. The current review highlights the need for larger multicenter and longitudinal studies, using complex approaches based on data-driven and unsupervised machine learning that will help to identify structural and functional patterns of brain degeneration. In turn, this may even allow for the prediction of subsequent phenoconversion from iRBD to the clinically defined synucleinopathie

Processing of primary and secondary rewards: A quantitative meta-analysis and review of human functional neuroimaging studies

One fundamental question concerning brain reward mechanisms is to determine how reward-related activity is influenced by the nature of rewards. Here, we review the neuroimaging literature and explicitly assess to what extent the representations of primary and secondary rewards overlap in the human brain. To achieve this goal, we performed an activation likelihood estimation (ALE) meta-analysis of 87 studies (1452 subjects) comparing the brain responses to monetary, erotic and food reward outcomes. Those three rewards robustly engaged a common brain network including the ventromedial prefrontal cortex, ventral striatum, amygdala, anterior insula and mediodorsal thalamus, although with some variations in the intensity and location of peak activity. Money-specific responses were further observed in the most anterior portion of the orbitofrontal cortex, supporting the idea that abstract secondary rewards are represented in evolutionary more recent brain regions. In contrast, food and erotic (i.e. primary) rewards were more strongly represented in the anterior insula, while erotic stimuli elicited particularly robust responses in the amygdala. Together, these results indicate that the computation of experienced reward value does not only recruit a core "reward system" but also reward type-dependent brain structures

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

Mental slowness and executive dysfunctions in patients with metabolic syndrome

Metabolic Syndrome is a cluster of vascular risk factors which has been related to dementia and cognitive decline. The aim of this study was to describe the neuropsychological profile of metabolic syndrome patients. An extensive neuropsychological protocol was administered to 55 patients and 35 controls assessing memory, executive, visuoperceptual and visuoconstructive functions, language and speed of processing. There were differences between groups in speed of processing and some executive functions after controlling for the influences of education and gender. The results suggest that metabolic syndrome may be a prodromal state of vascular cognitive impairment

Neuroimaging studies of cognitive remediation in schizophrenia: A systematic and critical review

AIM: To examine the effects of cognitive remediation therapies on brain functioning through neuroimaging procedures in patients with schizophrenia. METHODS: A systematic, computerised literature search was conducted in the PubMed/Medline and PsychInfo databases. The search was performed through February 2016 without any restrictions on language or publication date. The search was performed using the following search terms: [('cogniti*' and 'remediation' or 'training' or 'enhancement') and ('fMRI' or 'MRI' or 'PET' or 'SPECT') and (schizophrenia or schiz*)]. The search was accompanied by a manual online search and a review of the references from each of the papers selected, and those papers fulfilling our inclusion criteria were also included. RESULTS: A total of 101 studies were found, but only 18 of them fulfilled the inclusion criteria. These studies indicated that cognitive remediation improves brain activation in neuroimaging studies. The most commonly reported changes were those that involved the prefrontal and thalamic regions. Those findings are in agreement with the hypofrontality hypothesis, which proposes that frontal hypoactivation is the underlying mechanism of cognitive impairments in schizophrenia. Nonetheless, great heterogeneity among the studies was found. They presented different hypotheses, different results and different findings. The results of more recent studies interpreted cognitive recovery within broader frameworks, namely, as amelioration of the efficiency of different networks. Furthermore, advances in neuroimaging methodologies, such as the use of whole-brain analysis, tractography, graph analysis, and other sophisticated methodologies of data processing, might be conditioning the interpretation of results and generating new theoretical frameworks. Additionally, structural changes were described in both the grey and white matter, suggesting a neuroprotective effect of cognitive remediation. Cognitive, functional and structural improvements tended to be positively correlated

Microstructural white matter changes in metabolic syndrome: A diffusion tensor imaging study

BACKGROUND: Although metabolic syndrome is associated with cardiovascular disease and stroke, limited information is available on specific brain damage in patients with this syndrome. We investigated the relationship of the syndrome with white matter (WM) alteration using a voxel-based approach with diffusion tensor imaging (DTI). METHODS: We compared fractional anisotropy (FA) and apparent diffusion coefficient (ADC) measurements of DTI in 19 patients with metabolic syndrome aged between 50 and 80 years and 19 age-matched controls without any vascular risk factors for the syndrome. RESULTS: Patients with metabolic syndrome showed an anterior-posterior pattern of deterioration in WM with reduced FA and increased ADC values compared with controls. WM changes were not related to any isolated vascular risk factor. CONCLUSION: Although the mechanism of this damage is not clear, the results indicate microstructural white matter alterations in patients with metabolic syndrome, mainly involving the frontal lobe

White matter fractional anisotropy is related to processing speed in metabolic syndrome patients: a case-control study

Background Metabolic Syndrome (MetSd) is a cluster of vascular risk factors that may influence cerebrovascular pathology during aging. Recently, microstructural white matter (WM) changes detected by diffusion tensor imaging (DTI) and processing speed deficits have been reported in MetSd patients. We aimed to test the relationship between WM alteration and cognitive impairment in these patients. Methods The sample comprised 38 subjects (19 patients aged between 50 and 80 years old, and 19 controls). All patients fulfilled National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP-III) criteria for MetSd. Speed of information processing was measured by the Symbol Digit Modalities Test (SDMT) and reaction time (RT) on the Continuous Performance Test (CPT-II) and the Grooved Pegboard Test (GPT). DTI images were acquired in a 3 Tesla Siemens Trio scanner. Voxelwise statistical analysis of the fractional anisotropy (FA) data was performed using the Tract-Based Spatial Statistics part of the FMRIB Software Library. A correlation analysis was performed between processing speed variables and FA values. Results There was a larger proportion of slow subjects (percentile below 25th) in the patient group (Chi2 = 7.125 p = 0.008). FA values correlated positively with SDMT in anterior and posterior parts of the corpus callosum, and RT CPT-II correlated negatively with FA values in the anterior corpus callosum (p < 0.05 corrected) in the patient group. Conclusion We found significant correlations between WM alterations and cognitive impairment in MetSd patients, especially in the frontal lobe. These findings highlight the importance of MetSd prevention and control due to its association with structural and functional damage in the central nervous system

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