Both functional and also more recently resting state magnetic resonance imaging have become established tools to investigate functional brain networks. Most studies use these tools to compare ...different populations without controlling for potential differences in underlying brain structure which might affect the functional measurements of interest. Here, we adapt a simulation approach combined with evaluation of real resting state magnetic resonance imaging data to investigate the potential impact of partial volume effects on established functional and resting state magnetic resonance imaging analyses. We demonstrate that differences in the underlying structure lead to a significant increase in detected functional differences in both types of analyses. Largest increases in functional differences are observed for highest signal-to-noise ratios and when signal with the lowest amount of partial volume effects is compared to any other partial volume effect constellation. In real data, structural information explains about 25% of within-subject variance observed in degree centrality--an established resting state connectivity measurement. Controlling this measurement for structural information can substantially alter correlational maps obtained in group analyses. Our results question current approaches of evaluating these measurements in diseased population with known structural changes without controlling for potential differences in these measurements.
Social brain, social dysfunction and social withdrawal Porcelli, Stefano; Van Der Wee, Nic; van der Werff, Steven ...
Neuroscience & biobehavioral reviews/Neuroscience and biobehavioral reviews,
February 2019, 2019-02-00, 20190201, Letnik:
97
Journal Article
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Odprti dostop
•The human brain shows several levels of specialization for social stimuli processing.•Social brain could be affected by several neuropsychiatric disorders.•Mechanisms underlying social dysfunction ...are largely similar across disorders.•Social dysfunction and social withdrawal may represent a transdiagnostic domain.•A better understanding of their biological underpinnings may lead to new treatments.
The human social brain is complex. Current knowledge fails to define the neurobiological processes underlying social behaviour involving the (patho-) physiological mechanisms that link system-level phenomena to the multiple hierarchies of brain function. Unfortunately, such a high complexity may also be associated with a high susceptibility to several pathogenic interventions. Consistently, social deficits sometimes represent the first signs of a number of neuropsychiatric disorders including schizophrenia (SCZ), Alzheimer’s disease (AD) and major depressive disorder (MDD) which leads to a progressive social dysfunction. In the present review we summarize present knowledge linking neurobiological substrates sustaining social functioning, social dysfunction and social withdrawal in major psychiatric disorders. Interestingly, AD, SCZ, and MDD affect the social brain in similar ways. Thus, social dysfunction and its most evident clinical expression (i.e., social withdrawal) may represent an innovative transdiagnostic domain, with the potential of being an independent entity in terms of biological roots, with the perspective of targeted interventions.
There remains much scientific, clinical, and ethical controversy concerning the use of electroconvulsive therapy (ECT) for psychiatric disorders stemming from a lack of information and knowledge ...about how such treatment might work, given its nonspecific and spatially unfocused nature. The mode of action of ECT has even been ascribed to a “barbaric” form of placebo effect. Here we show differential, highly specific, spatially distributed effects of ECT on regional brain structure in two populations: patients with unipolar or bipolar disorder. Unipolar and bipolar disorders respond differentially to ECT and the associated local brain-volume changes, which occur in areas previously associated with these diseases, correlate with symptom severity and the therapeutic effect. Our unique evidence shows that electrophysical therapeutic effects, although applied generally, take on regional significance through interactions with brain pathophysiology.
Abstract
Postpartum depression (PPD) affects approximately 1 in 10 women after childbirth. A thorough understanding of a preexisting vulnerability to PPD will likely aid the early detection and ...treatment of PPD. Using a within-sample association, the study examined whether the brain’s structural and functional alterations predict the onset of depression. 157 euthymic postpartum women were subjected to a multimodal MRI scan within the first 6 days of childbirth and were followed up for 12 weeks. Based on a clinical interview 12 weeks postpartum, participants were classified as mentally healthy or having either PPD or adjustment disorder (AD). Voxel-based morphometry and resting-state functional connectivity comparisons were performed between the three groups. 13.4% of women in our study developed PPD (n = 21) and 12.1% (n = 19) adjustment disorder (AD). The risk factors for PPD were a psychiatric history and the experience and severity of baby blues and the history of premenstrual syndrome. Despite the different risk profiles, no differences between the PPD, AD and control group were apparent based on structural and functional neuroimaging data immediately after childbirth. At 12 weeks postpartum, a significant association was observed between Integrated Local Correlation (LCor) and the Edinburgh Postnatal Depression Score (EPDS). Our findings do not support the notion that the brain’s structural and resting-state functional alterations, if present, can be used as an early biomarker of PPD or AD. However, effects may become apparent if continuous measures of symptom severity are chosen.
Abstract We investigated morphometric brain changes in patients with Parkinson's disease (PD) that are associated with balance training. A total of 20 patients and 16 healthy matched controls learned ...a balance task over a period of 6 weeks. Balance testing and structural magnetic resonance imaging were performed before and after 2, 4, and 6 training weeks. Balance performance was re-evaluated after ∼20 months. Balance training resulted in performance improvements in both groups. Voxel-based morphometry revealed learning-dependent gray matter changes in the left hippocampus in healthy controls. In PD patients, performance improvements were correlated with gray matter changes in the right anterior precuneus, left inferior parietal cortex, left ventral premotor cortex, bilateral anterior cingulate cortex, and left middle temporal gyrus. Furthermore, a TIME × GROUP interaction analysis revealed time-dependent gray matter changes in the right cerebellum. Our results highlight training-induced balance improvements in PD patients that may be associated with specific patterns of structural brain plasticity. In summary, we provide novel evidence for the capacity of the human brain to undergo learning-related structural plasticity even in a pathophysiological disease state such as in PD.
Abstract The application of support vector machine classification (SVM) to combined information from magnetic resonance imaging (MRI) and F18fluorodeoxyglucose positron emission tomography (FDG-PET) ...has been shown to improve detection and differentiation of Alzheimer's disease dementia (AD) and frontotemporal lobar degeneration. To validate this approach for the most frequent dementia syndrome AD, and to test its applicability to multicenter data, we randomly extracted FDG-PET and MRI data of 28 AD patients and 28 healthy control subjects from the database provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and compared them to data of 21 patients with AD and 13 control subjects from our own Leipzig cohort. SVM classification using combined volume-of-interest information from FDG-PET and MRI based on comprehensive quantitative meta-analyses investigating dementia syndromes revealed a higher discrimination accuracy in comparison to single modality classification. For the ADNI dataset accuracy rates of up to 88% and for the Leipzig cohort of up to 100% were obtained. Classifiers trained on the ADNI data discriminated the Leipzig cohorts with an accuracy of 91%. In conclusion, our results suggest SVM classification based on quantitative meta-analyses of multicenter data as a valid method for individual AD diagnosis. Furthermore, combining imaging information from MRI and FDG-PET might substantially improve the accuracy of AD diagnosis.
Background
Digital biomarkers (DB), as captured using sensors embedded in modern smart devices, are a promising technology for home-based sign and symptom monitoring in Parkinson disease (PD).
...Objective
Despite extensive application in recent studies, test-retest reliability and longitudinal stability of DB have not been well addressed in this context. We utilized the large-scale m-Power data set to establish the test-retest reliability and longitudinal stability of gait, balance, voice, and tapping tasks in an unsupervised and self-administered daily life setting in patients with PD and healthy controls (HC).
Methods
Intraclass correlation coefficients were computed to estimate the test-retest reliability of features that also differentiate between patients with PD and healthy volunteers. In addition, we tested for longitudinal stability of DB measures in PD and HC, as well as for their sensitivity to PD medication effects.
Results
Among the features differing between PD and HC, only a few tapping and voice features had good to excellent test-retest reliabilities and medium to large effect sizes. All other features performed poorly in this respect. Only a few features were sensitive to medication effects. The longitudinal analyses revealed significant alterations over time across a variety of features and in particular for the tapping task.
Conclusions
These results indicate the need for further development of more standardized, sensitive, and reliable DB for application in self-administered remote studies in patients with PD. Motivational, learning, and other confounders may cause variations in performance that need to be considered in DB longitudinal applications.
In recent research, many univariate and multivariate approaches have been proposed to improve automatic classification of various dementia syndromes using imaging data. Some of these methods do not ...provide the possibility to integrate possible confounding variables like age into the statistical evaluation. A similar problem sometimes exists in clinical studies, as it is not always possible to match different clinical groups to each other in all confounding variables, like for example, early-onset (age<65 years) and late-onset (age≥65) patients with Alzheimer's disease (AD). Here, we propose a simple method to control for possible effects of confounding variables such as age prior to statistical evaluation of magnetic resonance imaging (MRI) data using support vector machine classification (SVM) or voxel-based morphometry (VBM). We compare SVM results for the classification of 80 AD patients and 79 healthy control subjects based on MRI data with and without prior age correction. Additionally, we compare VBM results for the comparison of three different groups of AD patients differing in age with the same group of control subjects obtained without including age as covariate, with age as covariate or with prior age correction using the proposed method. SVM classification using the proposed method resulted in higher between-group classification accuracy compared to uncorrected data. Further, applying the proposed age correction substantially improved univariate detection of disease-related grey matter atrophy using VBM in AD patients differing in age from control subjects. The results suggest that the approach proposed in this work is generally suited to control for confounding variables such as age in SVM or VBM analyses. Accordingly, the approach might improve and extend the application of these methods in clinical neurosciences.
Chemoarchitecture, the heterogeneous distribution of neurotransmitter transporter and receptor molecules, is a relevant component of structure-function relationships in the human brain. Here, we ...studied the organization of the receptome, a measure of interareal chemoarchitectural similarity, derived from positron-emission tomography imaging studies of 19 different neurotransmitter transporters and receptors. Nonlinear dimensionality reduction revealed three main spatial gradients of cortical chemoarchitectural similarity - a centro-temporal gradient, an occipito-frontal gradient, and a temporo-occipital gradient. In subcortical nuclei, chemoarchitectural similarity distinguished functional communities and delineated a striato-thalamic axis. Overall, the cortical receptome shared key organizational traits with functional and structural brain anatomy, with node-level correspondence to functional, microstructural, and diffusion MRI-based measures decreasing along a primary-to-transmodal axis. Relative to primary and paralimbic regions, unimodal and heteromodal regions showed higher receptomic diversification, possibly supporting functional flexibility.
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