应用GQI技术对小脑髓质的初步研究

目的　应用基于GQI的弥散成像方法,对小脑的纤维束进行重建和显示,为小脑疾病的影像诊断进行解剖学和技术上的探索。  方法　首先把10例脑连接组项目的磁共振弥散数据导入DSI-studio软件,然后依次用该软件中的GQI（Generalized Q-sampling imaging）成像技术和流线算法（streamline algorithm）对与小脑有关的纤维束进行重建和显示。  结果　（1）在DSI-studio软件的二维和三维界面中显示了小脑三对脚的位置和交叉情况;（2）显示了小脑3对脚与脊髓、延髓、脑桥、中脑、端脑等的连接情况及其主要纤维成分。  结论　应用GQI技术可以对小脑白质进行重建和显示,为小脑疾病的影像学诊断提供技术和结构学上的帮助。     

Auditory cortex hyperconnectivity before rTMS is correlated with tinnitus improvement

IntroductionRepetitive transcranial magnetic stimulation (rTMS) has been used as a potential treatment for tinnitus; however, its effectiveness is variable and unpredictable. We hypothesized that resting-state functional connectivity before rTMS may be correlated with rTMS treatment effectiveness.MethodsWe applied 1-Hz rTMS to the left primary auditory (A1) and dorsolateral prefrontal cortices (DLPFC) of 10 individuals with tinnitus and 10 age-matched controls. Resting-state functional magnetic resonance imaging (fMRI) studies were performed approximately one week before rTMS. Seed-based connectivity analyses were conducted for each individual, with seed regions as rTMS target areas.ResultsCompared to controls, the left superior temporal areas showed significantly increased positive connectivity with the left A1 and negative connectivity with the left DLPFC in the tinnitus group. The left frontoparietal and right cerebellar areas showed significantly increased negative connectivity with the left A1 and positive connectivity with the left DLPFC. Seed-based hyperconnectivity was correlated with tinnitus improvement (pre-rTMS vs. 2-week post-rTMS Tinnitus Handicap Inventory scores). Tinnitus improvement was significantly correlated with left A1 hyperconnectivity; however, no correlation was observed with left DLPFC connectivity. Positive rTMS outcomes were associated with significantly increased positive connectivity in bilateral superior temporal areas and significantly increased negative connectivity in bilateral frontal areas.ConclusionsOur results suggest that oversynchronisation of left A1 connectivity before rTMS of the left A1 and DLPFC is associated with treatment effectiveness.     

Predicting individual variability in task‐evoked brain activity in schizophrenia

What goes wrong in a schizophrenia patient''s brain that makes it so different from a healthy brain? In this study, we tested the hypothesis that the abnormal brain activity in schizophrenia is tightly related to alterations in brain connectivity. Using functional magnetic resonance imaging (fMRI), we demonstrated that both resting‐state functional connectivity and brain activity during the well‐validated N‐back task differed significantly between schizophrenia patients and healthy controls. Nevertheless, using a machine‐learning approach we were able to use resting‐state functional connectivity measures extracted from healthy controls to accurately predict individual variability in the task‐evoked brain activation in the schizophrenia patients. The predictions were highly accurate, sensitive, and specific, offering novel insights regarding the strong coupling between brain connectivity and activity in schizophrenia. On a practical perspective, these findings may allow to generate task activity maps for clinical populations without the need to actually perform any tasks, thereby reducing patients inconvenience while saving time and money.     

Emotional detachment,gait ataxia,and cerebellar dysconnectivity associated with compound heterozygous mutations in the SPG7 gene

ABSTRACT

We report a patient with autism-like deficits in emotional connectedness, executive dysfunction, and ataxia beginning at age 39. He had compound heterozygous variants in SPG7 (A510V and 1552+1 G>T substitutions), mutation of which is classically associated with spastic paraparesis. Diffusion MRI demonstrated abnormalities in the cerebellar outflow tracts. Transcranial magnetic stimulation showed a prolonged cortical silent period representing exaggerated cortical inhibition, as previously described with pure cerebellar degeneration. The acquired cerebellar cognitive affective syndrome in association with specific anatomic and neurophysiological abnormalities in the cerebellum expand the spectrum of SPG7-related neurodegeneration and support a role for cerebellar output in socio-emotional behavior.     

Restoration of functional network state towards more physiological condition as the correlate of clinical effects of pallidal deep brain stimulation in dystonia

BackgroundDeep brain stimulation of the internal globus pallidus (GPi DBS) is an invasive therapeutic modality intended to retune abnormal central nervous system patterns and relieve the patient of dystonic or other motor symptoms.ObjectivesThe aim of the presented research was to determine the neuroanatomical signature of GPi DBS modulation and its association with the clinical outcome.MethodsThis open-label fixed-order study with cross-sectional validation against healthy controls analysed the resting-state functional MRI activity changes induced by GPi DBS in 18 dystonia patients of heterogeneous aetiology, focusing on both global (full brain) and local connectivity (local signal homogeneity).ResultsCompared to the switched-off state, the activation of GPi DBS led to the restoration of global subcortical connectivity patterns (in both putamina, diencephalon and brainstem) towards those of healthy controls, with positive direct correlation over large-scale cortico-basal ganglia-thalamo-cortical and cerebellar networks with the clinical improvement. Nonetheless, on average, GPi DBS also seemed to bring local connectivity both in the cortical and subcortical regions farther away from the state detected in healthy controls. Interestingly, its correlation with clinical outcome showed that in better DBS responders, local connectivity defied this effect and approached healthy controls.ConclusionsAll in all, the extent of restoration of both these main metrics of interest towards the levels found in healthy controls clearly correlated with the clinical improvement, indicating that the restoration of network state towards more physiological condition may be a precondition for successful GPi DBS outcome in dystonia.     

Structural and functional connectivity of the precuneus and thalamus to the default mode network

Neuroimaging studies have identified functional interactions between the thalamus, precuneus, and default mode network (DMN) in studies of consciousness. However, less is known about the structural connectivity of the precuneus and thalamus to regions within the DMN. We used diffusion tensor imaging (DTI) to parcellate the precuneus and thalamus based on their probabilistic white matter connectivity to each other and DMN regions of interest (ROIs) in 37 healthy subjects from the Human Connectome Database. We further assessed resting‐state functional connectivity (RSFC) among the precuneus, thalamus, and DMN ROIs. The precuneus was found to have the greatest structural connectivity with the thalamus, where connection fractional anisotropy (FA) increased with age. The precuneus also showed significant structural connectivity to the hippocampus and middle pre‐frontal cortex, but minimal connectivity to the angular gyrus and midcingulate cortex. In contrast, the precuneus exhibited significant RSFC with the thalamus and the strongest RSFC with the AG. Significant symmetrical structural connectivity was found between the thalamus and hippocampus, mPFC, sFG, and precuneus that followed known thalamocortical pathways, while thalamic RSFC was strongest with the precuneus and hippocampus. Overall, these findings reveal high levels of structural and functional connectivity linking the thalamus, precuneus, and DMN. Differences between structural and functional connectivity (such as between the precuneus and AG) may be interpreted to reflect dynamic shifts in RSFC for cortical hub‐regions involved with consciousness, but could also reflect the limitations of DTI to detect superficial white matter tracts that connect cortico‐cortical regions. Hum Brain Mapp 38:938–956, 2017. © 2016 Wiley Periodicals, Inc.     

Disconnectome of the migraine brain: a “connectopathy” model

BackgroundIn the past decades a plethora of studies has been conducted to explore resting-state functional connectivity (RS-FC) of the brain networks in migraine with conflicting results probably due to the variability and susceptibility of signal fluctuations across the course of RS-FC scan. On the other hand, the structural substrates enabling the functional communications among the brain connectome, characterized by higher stability and reproducibility, have not been widely investigated in migraine by means of graph analysis approach. We hypothesize a rearrangement of the brain connectome with an increase of both strength and density of connections between cortical areas specifically involved in pain perception, processing and modulation in migraine patients. Moreover, such connectome rearrangement, inducing an imbalance between the competing parameters of network efficiency and segregation, may underpin a mismatch between energy resources and demand representing the neuronal correlate of the energetically dysfunctional migraine brain.MethodsWe investigated, using diffusion-weighted MRI imaging tractography-based graph analysis, the graph-topological indices of the brain “connectome”, a set of grey matter regions (nodes) structurally connected by white matter paths (edges) in 94 patients with migraine without aura compared to 91 healthy controls.ResultsWe observed in migraine patients compared to healthy controls: i) higher local and global network efficiency (p < 0.001) and ii) higher local and global clustering coefficient (p < 0.001). Moreover, we found changes in the hubs topology in migraine patients with: i) posterior cingulate cortex and inferior parietal lobule (encompassing the so-called neurolimbic-pain network) assuming the hub role and ii) fronto-orbital cortex, involved in emotional aspects, and visual areas, involved in migraine pathophysiology, losing the hub role. Finally, we found higher connection (edges) probability between cortical nodes involved in pain perception and modulation as well as in cognitive and affective attribution of pain experiences, in migraine patients when compared to healthy controls (p < 0.001). No correlations were found between imaging and clinical parameters of disease severity.ConclusionThe imbalance between the need of investing resources to promote network efficiency and the need of minimizing the metabolic cost of wiring probably represents the mechanism underlying migraine patients’ susceptibility to triggers. Such changes in connectome topography suggest an intriguing pathophysiological model of migraine as brain “connectopathy”.Supplementary InformationThe online version contains supplementary material available at 10.1186/s10194-021-01315-6.     

The influence of head motion on intrinsic functional connectivity MRI

Functional connectivity MRI (fcMRI) has been widely applied to explore group and individual differences. A confounding factor is head motion. Children move more than adults, older adults more than younger adults, and patients more than controls. Head motion varies considerably among individuals within the same population. Here we explored the influence of head motion on fcMRI estimates. Mean head displacement, maximum head displacement, the number of micro movements (> 0.1 mm), and head rotation were estimated in 1000 healthy, young adult subjects each scanned for two resting-state runs on matched 3T scanners. The majority of fcMRI variation across subjects was not linked to head motion. However, head motion had significant, systematic effects on fcMRI network measures. Head motion was associated with decreased functional coupling in the default and frontoparietal control networks — two networks characterized by coupling among distributed regions of association cortex. Other network measures increased with motion including estimates of local functional coupling and coupling between left and right motor regions — a region pair sometimes used as a control in studies to establish specificity. Comparisons between groups of individuals with subtly different levels of head motion yielded difference maps that could be mistaken for neuronal effects in other contexts. These effects are important to consider when interpreting variation between groups and across individuals.     

Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi‐group comparison

A large number of mathematical models have been proposed to describe the measured signal in diffusion‐weighted (DW) magnetic resonance imaging (MRI). However, model comparison to date focuses only on specific subclasses, e.g. compartment models or signal models, and little or no information is available in the literature on how performance varies among the different types of models. To address this deficiency, we organized the ‘White Matter Modeling Challenge’ during the International Symposium on Biomedical Imaging (ISBI) 2015 conference. This competition aimed to compare a range of different kinds of models in their ability to explain a large range of measurable in vivo DW human brain data. Specifically, we assessed the ability of models to predict the DW signal accurately for new diffusion gradients and b values. We did not evaluate the accuracy of estimated model parameters, as a ground truth is hard to obtain. We used the Connectome scanner at the Massachusetts General Hospital, using gradient strengths of up to 300 mT/m and a broad set of diffusion times. We focused on assessing the DW signal prediction in two regions: the genu in the corpus callosum, where the fibres are relatively straight and parallel, and the fornix, where the configuration of fibres is more complex. The challenge participants had access to three‐quarters of the dataset and their models were ranked on their ability to predict the remaining unseen quarter of the data. The challenge provided a unique opportunity for a quantitative comparison of diverse methods from multiple groups worldwide. The comparison of the challenge entries reveals interesting trends that could potentially influence the next generation of diffusion‐based quantitative MRI techniques. The first is that signal models do not necessarily outperform tissue models; in fact, of those tested, tissue models rank highest on average. The second is that assuming a non‐Gaussian (rather than purely Gaussian) noise model provides little improvement in prediction of unseen data, although it is possible that this may still have a beneficial effect on estimated parameter values. The third is that preprocessing the training data, here by omitting signal outliers, and using signal‐predicting strategies, such as bootstrapping or cross‐validation, could benefit the model fitting. The analysis in this study provides a benchmark for other models and the data remain available to build up a more complete comparison in the future.