Dopaminergic modulation of cortical motor network lateralization

Introduction Unilateral movements are primarily processed in contralateral cortical and subcortical areas and additionally in ipsilateral cerebellum, leading to an asymmetric pattern of neural activation. Decrease of lateralization is characteristic of aging (Naccarato et al., 2006; Wu et al., 2005), and disease, for example, in unilateral brain lesions or stroke (Carr et al., 1993; Rehme et al., 2011) and Parkinson's disease (PD; Wu et al., 2015). The explanation for imbalanced lateralization in drug-naive PD is an adaptive compensation, compatible with the finding that PD-associated deficient input from cortico-subcortical circuits is compensated by reduced cortical inhibition and increased cortical facilitation (Blesa et al., 2017). Here, we investigated the effect of dopamine depletion and substitution on cortical motor lateralization, with the hypothesis that lateralization decreases in advanced PD and that administration of levodopa, at least to a certain extent, reinstates lateralization. Methods We used fMRI to study motor activation in advanced PD patients and in healthy controls (HC) during unilateral upper and lower limb movements. Ten right-handed, left side symptom-dominant PD patients were tested in pseudo-randomized order after intake of their usual dopaminergic medication – 'ON' state – and after withdrawal of medication – 'OFF' state. Eighteen right-handed age-matched HC participated in a single session. We quantified activation lateralization using the average laterality index (AveLI; Matsuo et al., 2012) in three cortical motor regions of interest (ROIs): primary motor cortex (M1), supplementary motor area (SMA) and premotor cortex (PMC), during the four movement conditions. We compared AveLI between group pairs (PD OFF vs. HC, PD ON vs. HC, PD OFF vs. PD ON) within each ROI and movement condition. We estimated the effective connectivity between ROIs using bilinear dynamic causal modeling (DCM; Friston et al., 2003) and developed a measure to quantify the lateralization of the resulting connectivity networks to compare between groups. By constructing a group level parametric empirical Bayes (PEB) model (Friston et al., 2016) over all the subjects and conducting a search over nested models, we compared DCM parameter estimates between groups, thus providing the potential link between changes in motor lateralization and connectivity. Results In line with our predictions, motor activation lateralization as estimated with the AveLI showed a trend towards decrease in the PD OFF group compared to HC, in all three ROIs during left hand movement and in M1 during left foot movement (Fig. 1). Between-group differences were observed solely in conditions corresponding to movement of the more affected body side. Contrary to our hypothesis, dopamine substitution did not reinstate lateralization – in fact, AveLI in the PD ON group closely resembled that of the PD OFF group. Connectivity lateralization of input-specific modulation (DCM.B) networks was significantly lower in all conditions in the PD group as compared to HC. While on the body side more affected by PD, differences were found for both PD OFF and PD ON, input-specific modulation related to the less affected side was more altered in PD ON. PEB analysis revealed qualitatively more between-group differences in input-specific modulation on the more affected PD side and included many interhemispheric connections (Fig. 2). Conclusions Decreased lateralization is not only present in drug-naïve PD patients (Wu et al., 2015) but also in dopa-treated patients. Acute dopamine modulation does not alter lateralization. Decreased lateralization is evident in both fMRI activation amplitudes (as estimated with AveLI) and effective connectivity (as demonstrated through the DCM analysis)

Evaluation of tractography-based myelin-weighted connectivity across the lifespan

IntroductionRecent studies showed that the myelin of the brain changes in the life span, and demyelination contributes to the loss of brain plasticity during normal aging. Diffusion-weighted magnetic resonance imaging (dMRI) allows studying brain connectivity in vivo by mapping axons in white matter with tractography algorithms. However, dMRI does not provide insight into myelin; thus, combining tractography with myelin-sensitive maps is necessary to investigate myelin-weighted brain connectivity. Tractometry is designated for this purpose, but it suffers from some serious limitations. Our study assessed the effectiveness of the recently proposed Myelin Streamlines Decomposition (MySD) method in estimating myelin-weighted connectomes and its capacity to detect changes in myelin network architecture during the process of normal aging. This approach opens up new possibilities compared to traditional Tractometry.MethodsIn a group of 85 healthy controls aged between 18 and 68 years, we estimated myelin-weighted connectomes using Tractometry and MySD, and compared their modulation with age by means of three well-known global network metrics.ResultsFollowing the literature, our results show that myelin development continues until brain maturation (40 years old), after which degeneration begins. In particular, mean connectivity strength and efficiency show an increasing trend up to 40 years, after which the process reverses. Both Tractometry and MySD are sensitive to these changes, but MySD turned out to be more accurate.ConclusionAfter regressing the known predictors, MySD results in lower residual error, indicating that MySD provides more accurate estimates of myelin-weighted connectivity than Tractometry

Severe Neuro-COVID is associated with peripheral immune signatures, autoimmunity and neurodegeneration: a prospective cross-sectional study

Growing evidence links COVID-19 with acute and long-term neurological dysfunction. However, the pathophysiological mechanisms resulting in central nervous system involvement remain unclear, posing both diagnostic and therapeutic challenges. Here we show outcomes of a cross-sectional clinical study (NCT04472013) including clinical and imaging data and corresponding multidimensional characterization of immune mediators in the cerebrospinal fluid (CSF) and plasma of patients belonging to different Neuro-COVID severity classes. The most prominent signs of severe Neuro-COVID are blood-brain barrier (BBB) impairment, elevated microglia activation markers and a polyclonal B cell response targeting self-antigens and non-self-antigens. COVID-19 patients show decreased regional brain volumes associating with specific CSF parameters, however, COVID-19 patients characterized by plasma cytokine storm are presenting with a non-inflammatory CSF profile. Post-acute COVID-19 syndrome strongly associates with a distinctive set of CSF and plasma mediators. Collectively, we identify several potentially actionable targets to prevent or intervene with the neurological consequences of SARS-CoV-2 infection

A Multicenter Longitudinal MRI Study Assessing LeMan-PV Software Accuracy in the Detection of White Matter Lesions in Multiple Sclerosis Patients.

BACKGROUND Detecting new and enlarged lesions in multiple sclerosis (MS) patients is needed to determine their disease activity. LeMan-PV is a software embedded in the scanner reconstruction system of one vendor, which automatically assesses new and enlarged white matter lesions (NELs) in the follow-up of MS patients; however, multicenter validation studies are lacking. PURPOSE To assess the accuracy of LeMan-PV for the longitudinal detection NEL white-matter MS lesions in a multicenter clinical setting. STUDY TYPE Retrospective, longitudinal. SUBJECTS A total of 206 patients with a definitive MS diagnosis and at least two follow-up MRI studies from five centers participating in the Swiss Multiple Sclerosis Cohort study. Mean age at first follow-up = 45.2 years (range: 36.9-52.8 years); 70 males. FIELD STRENGTH/SEQUENCE Fluid attenuated inversion recovery (FLAIR) and T1-weighted magnetization prepared rapid gradient echo (T1-MPRAGE) sequences at 1.5 T and 3 T. ASSESSMENT The study included 313 MRI pairs of datasets. Data were analyzed with LeMan-PV and compared with a manual "reference standard" provided by a neuroradiologist. A second rater (neurologist) performed the same analysis in a subset of MRI pairs to evaluate the rating-accuracy. The Sensitivity (Se), Specificity (Sp), Accuracy (Acc), F1-score, lesion-wise False-Positive-Rate (aFPR), and other measures were used to assess LeMan-PV performance for the detection of NEL at 1.5 T and 3 T. The performance was also evaluated in the subgroup of 123 MRI pairs at 3 T. STATISTICAL TESTS Intraclass correlation coefficient (ICC) and Cohen's kappa (CK) were used to evaluate the agreement between readers. RESULTS The interreader agreement was high for detecting new lesions (ICC = 0.97, Pvalue < 10-20 , CK = 0.82, P value = 0) and good (ICC = 0.75, P value < 10-12 , CK = 0.68, P value = 0) for detecting enlarged lesions. Across all centers, scanner field strengths (1.5 T, 3 T), and for NEL, LeMan-PV achieved: Acc = 61%, Se = 65%, Sp = 60%, F1-score = 0.44, aFPR = 1.31. When both follow-ups were acquired at 3 T, LeMan-PV accuracy was higher (Acc = 66%, Se = 66%, Sp = 66%, F1-score = 0.28, aFPR = 3.03). DATA CONCLUSION In this multicenter study using clinical data settings acquired at 1.5 T and 3 T, and variations in MRI protocols, LeMan-PV showed similar sensitivity in detecting NEL with respect to other recent 3 T multicentric studies based on neural networks. While LeMan-PV performance is not optimal, its main advantage is that it provides automated clinical decision support integrated into the radiological-routine flow. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2

Automated Discrimination of Brain Pathological State Attending to Complex Structural Brain Network Properties: The Shiverer Mutant Mouse Case

Neuroimaging classification procedures between normal and pathological subjects are sparse and highly dependent of an expert's clinical criterion. Here, we aimed to investigate whether possible brain structural network differences in the shiverer mouse mutant, a relevant animal model of myelin related diseases, can reflect intrinsic individual brain properties that allow the automatic discrimination between the shiverer and normal subjects. Common structural networks properties between shiverer (C3Fe.SWV Mbpshi/Mbpshi, n = 6) and background control (C3HeB.FeJ, n = 6) mice are estimated and compared by means of three diffusion weighted MRI (DW-MRI) fiber tractography algorithms and a graph framework. Firstly, we found that brain networks of control group are significantly more clustered, modularized, efficient and optimized than those of the shiverer group, which presented significantly increased characteristic path length. These results are in line with previous structural/functional complex brain networks analysis that have revealed topologic differences and brain network randomization associated to specific states of human brain pathology. In addition, by means of network measures spatial representations and discrimination analysis, we show that it is possible to classify with high accuracy to which group each subject belongs, providing also a probability value of being a normal or shiverer subject as an individual anatomical classifier. The obtained correct predictions (e.g., around 91.6–100%) and clear spatial subdivisions between control and shiverer mice, suggest that there might exist specific network subspaces corresponding to specific brain disorders, supporting also the point of view that complex brain network analyses constitutes promising tools in the future creation of interpretable imaging biomarkers

Effects of eight neuropsychiatric copy number variants on human brain structure

Many copy number variants (CNVs) confer risk for the same range of neurodevelopmental symptoms and psychiatric conditions including autism and schizophrenia. Yet, to date neuroimaging studies have typically been carried out one mutation at a time, showing that CNVs have large effects on brain anatomy. Here, we aimed to characterize and quantify the distinct brain morphometry effects and latent dimensions across 8 neuropsychiatric CNVs. We analyzed T1-weighted MRI data from clinically and non-clinically ascertained CNV carriers (deletion/duplication) at the 1q21.1 (n = 39/28), 16p11.2 (n = 87/78), 22q11.2 (n = 75/30), and 15q11.2 (n = 72/76) loci as well as 1296 non-carriers (controls). Case-control contrasts of all examined genomic loci demonstrated effects on brain anatomy, with deletions and duplications showing mirror effects at the global and regional levels. Although CNVs mainly showed distinct brain patterns, principal component analysis (PCA) loaded subsets of CNVs on two latent brain dimensions, which explained 32 and 29% of the variance of the 8 Cohen’s d maps. The cingulate gyrus, insula, supplementary motor cortex, and cerebellum were identified by PCA and multi-view pattern learning as top regions contributing to latent dimension shared across subsets of CNVs. The large proportion of distinct CNV effects on brain morphology may explain the small neuroimaging effect sizes reported in polygenic psychiatric conditions. Nevertheless, latent gene brain morphology dimensions will help subgroup the rapidly expanding landscape of neuropsychiatric variants and dissect the heterogeneity of idiopathic conditions

Statistical analysis of multichannel scalp field data

High density spatial and temporal sampling of EEG data enhances the quality of results of electrophysiological experiments. Because EEG sources typically produce widespread electric fields (see Chapter 3) and operate at frequencies well below the sampling rate, increasing the number of electrodes and time samples will not necessarily increase the number of observed processes, but mainly increase the accuracy of the representation of these processes. This is namely the case when inverse solutions are computed. As a consequence, increasing the sampling in space and time increases the redundancy of the data (in space, because electrodes are correlated due to volume conduction, and time, because neighboring time points are correlated), while the degrees of freedom of the data change only little. This has to be taken into account when statistical inferences are to be made from the data. However, in many ERP studies, the intrinsic correlation structure of the data has been disregarded. Often, some electrodes or groups of electrodes are a priori selected as the analysis entity and considered as repeated (within subject) measures that are analyzed using standard univariate statistics. The increased spatial resolution obtained with more electrodes is thus poorly represented by the resulting statistics. In addition, the assumptions made (e.g. in terms of what constitutes a repeated measure) are not supported by what we know about the properties of EEG data. From the point of view of physics (see Chapter 3), the natural “atomic” analysis entity of EEG and ERP data is the scalp electric fiel

Glucose Metabolism during Resting State Reveals Abnormal Brain Networks Organization in the Alzheimer’s Disease and Mild Cognitive Impairment

<div><p>This paper aims to study the abnormal patterns of brain glucose metabolism co-variations in Alzheimer disease (AD) and Mild Cognitive Impairment (MCI) patients compared to Normal healthy controls (NC) using the Alzheimer Disease Neuroimaging Initiative (ADNI) database. The local cerebral metabolic rate for glucose (CMRgl) in a set of 90 structures belonging to the AAL atlas was obtained from Fluro-Deoxyglucose Positron Emission Tomography data in resting state. It is assumed that brain regions whose CMRgl values are significantly correlated are functionally associated; therefore, when metabolism is altered in a single region, the alteration will affect the metabolism of other brain areas with which it interrelates. The glucose metabolism network (represented by the matrix of the CMRgl co-variations among all pairs of structures) was studied using the graph theory framework. The highest concurrent fluctuations in CMRgl were basically identified between homologous cortical regions in all groups. Significant differences in CMRgl co-variations in AD and MCI groups as compared to NC were found. The AD and MCI patients showed aberrant patterns in comparison to NC subjects, as detected by global and local network properties (global and local efficiency, clustering index, and others). MCI network’s attributes showed an intermediate position between NC and AD, corroborating it as a transitional stage from normal aging to Alzheimer disease. Our study is an attempt at exploring the complex association between glucose metabolism, CMRgl covariations and the attributes of the brain network organization in AD and MCI.</p></div

Competition for neuronal resources: how hallucinations make themselves heard

BACKGROUND: Hallucinations are perceptions in the absence of a corresponding external sensory stimulus. However, during auditory verbal hallucinations, activation of the primary auditory cortex has been described. AIMS: The objective of this study was to investigate whether this activation of the auditory cortex contributes essentially to the character of hallucinations and attributes them to alien sources, or whether the auditory activation is a sign of increased general auditory attention to external sounds. METHOD: The responsiveness of the auditory cortex was investigated by auditory evoked potentials (N100) during the simultaneous occurrence of hallucinations and external stimuli. Evoked potentials were computed separately for periods with and without hallucinations; N100 power, topography and brain electrical sources were analysed. RESULTS: Hallucinations lowered the N100 amplitudes and changed the topography, presumably due to a reduced left temporal responsivity. CONCLUSIONS: This finding indicates competition between auditory stimuli and hallucinations for physiological resources in the primary auditory cortex. The abnormal activation of the primary auditory cortex may thus be a constituent of auditory hallucinations