Neuroimage. Reports最新文献

Despite the kraepelinian differentiation of schizophrenia and bipolar disorder, several data questioned this net subdivision and suggested a continuity between the two. An expanded continuum hypothesis was suggested, assuming a common psychotic core between the two disorders, as well as cognitive and affective differences. The present study aimed to investigate similarities and differences between schizophrenia and bipolar disorder for what entails the affective dimension of the continuum. A coordinate-based meta-analytic approach on neuroimaging data was applied to understand differences and similarities in the visual perception of negative stimuli in the two groups. The activation likelihood estimation analysis included 41 experiments on schizophrenia (schizophrenia versus healthy controls) and 27 experiments on bipolar disorder (bipolar versus healthy controls). Our conjunction analysis results revealed the presence of shared functional abnormalities in thalamic, parahippocampal, and basal ganglia areas, suggesting that these patients share an altered circuit responsible for a heightened elaboration of negative emotional stimuli. The subtraction analysis highlighted that the two groups present differences too. Schizophrenia patients show widespread abnormalities in limbic, temporal, sub-lobar and midbrain regions possibly involved in emotional processing and hallucinations. On the other hand, bipolar patients show alterations in frontal areas associated with emotional appraisal, regulation, and response inhibition. This study sheds light on both similarities and differences in the emotional processing of schizophrenic and bipolar patients, and may help to better characterise the affective features of these two conditions along a continuum.

Background

Several studies on patients with Alzheimer's disease (AD) have used transcranial direct current stimulation (tDCS) to enhance neural excitability in the left dorsolateral prefrontal cortex (lDLPFC). Interindividual differences in brain anatomy in AD patients pose a challenge to efficiently target the lDLPFC using scalp-based coordinates, calling for new and more precise tDCS protocols.

Objective

The purpose of this study was to explore how AD-related neuropathology affects the tDCS-induced electric field (EF) across different DLPFC montages using computational modeling.

Method

Forty-eight realistic head models were created from structural magnetic resonance scans of AD patients and healthy controls collected from a publicly available database. We compared the tDCS-induced EF in different montages applied in the literature, in addition to a high definition (HD)-tDCS montage centered at electrode F3.

Results

There was an overall global reduction in EF strength in the patient group, probably due to structural alterations that were also identified in the patient group. A widespread distribution of the EF was found across the frontal lobe for bipolar montages, while HD-tDCS yielded more focal stimulation, mainly restricted to the lDLPFC. Minor differences in the EF distribution were found when comparing the HD-tDCS montages.

Conclusion

Neurodegenerative alterations present in patients with AD affect the magnitude, distribution and variability of the EF. HD-tDCS montages provide more focal stimulation of the target area, compared to bipolar montages with to pronounced group differences between AD patients and healthy matched controls. This finding poses substantial limitations to the comparison of cognitive effects of tDCS both between patients and controls and within patients at different stages of disease progression.

Survivors of pediatric brain tumors often live with long-term cognitive difficulties related to brain changes associated with the tumor itself as well as treatments such as radiation therapy. The present study used graph theory to examine functional network properties in this population and whether graph metrics relate to core cognitive skills: attention, working memory, and processing speed. 31 survivors and 31 matched controls completed neuropsychological testing and functional magnetic resonance imaging. Neuroimaging was preprocessed and spatially constrained ICA was completed, followed by calculation of area under the curve values of graph metrics. Results revealed a significant difference such that brain tumor survivors exhibited less small-world properties. This was found to be related to working memory, such that less small-worldness in the network was related to poorer performance. Furthermore, hub regions appear to be particularly vulnerable to disruption. Comparison to results of microstructural network analysis from a similar sample suggest functional connectivity graph metrics provide different and complementary information and additional post-hoc analyses are also discussed. These findings reveal that survivors of pediatric brain tumor indeed display significant differences in functional brain networks that are quantifiable by graph theory and build a foundation to better understand how metrics such as small-worldness can be used to predict long-term cognitive outcomes in adulthood. Ongoing neuroimaging research may play a part in precision medicine determining treatment protocols and interventions for pediatric brain tumor patients.

Background

Brain MRI in infants at ultra-high-field scanners might improve diagnostic quality, but safety should be evaluated first. In our previous study, we reported simulated specific absorption rates and acoustic noise data at 7 Tesla.

Methods

In this study, we included twenty infants between term-equivalent age and three months of age. The infants were scanned on a 7 Tesla MRI directly after their clinically indicated 3 Tesla brain MRI scan. Vital parameters, temperature, and comfort were monitored throughout the process. Brain temperature was estimated during the MRI scans using proton MR spectroscopy.

Results

We found no significant differences in vital parameters, temperature, and comfort during and after 7 Tesla MRI scans, compared to 3 Tesla MRI scans.

Conclusions

These data confirm our hypothesis that scanning infants at 7 Tesla MRI appears to be safe and we identified no additional risks from scanning at 3 Tesla MRI.

Trait ‘absorption’ is a psychological construct with a rich history that was initially born from early work on hypnotic suggestibility. Absorption characterizes an individual's tendency to become effortlessly engrossed in the contents of experience, whether in terms of external sensory phenomena or internal imagery and fantasy, and is reliably associated with a constellation of psychological, cognitive, and behavioral traits. Here, we conducted a comprehensive neuroimaging investigation of associations between trait absorption and the brain. In particular, we assessed multivariate relationships between absorption scores and neuroimaging measures of grey matter density, as well as static and dynamic resting-state functional connectivity. We investigated these relationships using partial least squares in a discovery dataset (n = 201) and then attempted to reproduce results in an independent replication dataset (n = 68). Results revealed a lack of significant associations between absorption and grey matter density across both datasets, and a significant association between absorption and static resting-state functional connectivity in the discovery dataset which was not replicated in the replication dataset. Additional control analyses further indicated the lack of a reliable brain-absorption relationship, whereas we found a replicable association between the closely related trait of ‘openness to experience’ and resting-state functional connectivity. We conclude that absorption is not reliably associated with brain structure or function in the present datasets and discuss factors that may have contributed to this result. This study serves as the first comprehensive and adequately powered investigation of the neural correlates of absorption and motivates future studies to refine the conceptualization of this perplexing trait.

The corpus callosum (CC) is one of the most important interhemispheric white matter tracts that connects interrelated regions of the cerebral cortex. Its disruption has been investigated in previous studies and has been found to play an important role in several neurodegenerative disorders. Currently available methods to assess the interhemispheric connectivity of the CC have several limitations: i) they require the a priori identification of specific cortical regions as targets or seeds, ii) they are limited by the characterization of only small components of the structure, primarily voxels that constitute the mid-sagittal slice, and iii) they use global measures of microstructural integrity, which provide only limited characterization. In order to address some of these limitations, we developed a novel method that enables the characterization of white matter tracts covering the structure of CC, from the mid-sagittal plane to corresponding regions of cortex, using directional tract density patterns (dTDPs). We demonstrate that different regions of CC have distinctive dTDPs that reflect a unique regional topology. We conducted a pilot study using this approach to evaluate two different datasets collected from healthy subjects, and we demonstrate that this method is reliable, reproducible, and independent of diffusion acquisition parameters, suggesting its potential applicability to clinical applications.

Neurodevelopment during early childhood and adolescence are recognised as critical periods, with potential life-long lasting impacts on mental health and wellbeing. The time-frame of these neurodevelopmental changes also correspond to one in five individuals aged 9–17 years old being diagnosed with a mental health condition. Furthermore, sex-based differences in the diagnosed prevalence of mental health conditions are also well characterised and can be leveraged to differentiate development of brain structures between sexes throughout childhood and adolescence. During adolescence, early observed mental health symptoms, alongside measures of brain development, may provide utility toward understanding both the onset timing of various mental conditions, and a neurobiological explanation for disproportionate prevalence's among sexes. This study aims to determine sex differences in psychological distress levels and structural brain volume relationships in early adolescents. To address this question, we first present and then utilise the ‘first hundred brains’ (FHB) cohort, a multimodal dataset of 12-to-13 year-olds individuals enrolled in the Longitudinal Adolescent Brain Study (LABS). The FHB dataset consists of 101 unique individuals (47 female), aged 13.01 ± 0.55 years. Psychological distress was measured using the Kessler-10, a self-report questionnaire probing recent experiences of anxiety and depression symptoms. All participants underwent 3T MRI brain scans. T1-weighted structural scans were processed using FreeSurfer's Sequence Adaptive Multimodal segmentation pipeline, with volume measurements from 39 regions of interest included in the analyses. Findings revealed that compared to age matched males, early adolescent females have significantly higher psychological distress as well as significantly larger hippocampi and ventral diencephalon, bilaterally. Correlational analyses revealed a significant positive association between psychological distress scores and right amygdala volumes for males, but not in females, or the combined cohort. In this initial analysis of the FHB dataset, we have identified significant sex differences in psychological distress, brain volumes, and the relationships between these two metrics. With the peak age-of-onset for many psychiatric disorders occurring during adolescence, research focused on youth mental health vulnerability and opportunity for early detection, prevention and improvement is vitally important.

The discrete behavioral events captured on the smartphone touchscreen may help unravel real-world neural processing. We find that neural signals (EEG) surrounding a touchscreen event show a distinctly contralateral motor preparation followed by visual processing, and the consolidation of information. We leveraged these events in conjunction with kinematic recordings of the thumb and an artificial neural network to separate highly similar movements according to whether they resulted in a smartphone touch (goal-directed) or not (non-goal-directed). Despite their kinematic similarity, the signatures of neural control of movement and the post-movement processing were substantially dampened for the non-goal-directed movements, and these movements uniquely evoked error-related signals. We speculate that these apparently unnecessary movements are common in the real world and although inconsequential the brain provides limited motor preparation and tracks the action outcome. The neural signals surrounding discrete smartphone events can enable the study of neural processes that are difficult to capture in conventional laboratory-based tasks.

As an active field of research and with the development of state-of-the-art algorithms to analyze EEG datasets, the parametrization of Electroencephalography (EEG) analysis workflows has become increasingly flexible and complex, with a great variety of methodological options and tools to be selected at each step. This high analytical flexibility can be problematic as it can yield to variability in research outcomes. Therefore, growing attention has been recently paid to understand the potential impact of different methodological decisions on the reproducibility of results.

In this paper, we aim to examine how sensitive the results of EEG analyses are to variations in preprocessing with different software tools. We reanalyzed the shared EEG data (N = 500) from (Williams et al., 2021) using three of the most commonly used open-source Matlab-based EEG software tools: EEGLAB, Brainstorm and FieldTrip. After reproducing the same original preprocessing workflow in each software, the resulting event-related potentials (ERPs) were qualitatively and quantitatively compared in order to examine the degree of consistency/discrepancy between software packages. Our findings show a good degree of convergence in terms of the general profile of ERP waveforms, peak latencies and effect size estimates related to specific signal features. However, considerable variability was also observed in the magnitude of the absolute voltage observed with each software package as reflected by the similarity values and observed statistical differences at particular channels and time instants. In conclusion, we believe that this study provides valuable clues to better understand the impact of the software tool on the analysis of EEG results.

Machine learning combined with large-scale neuroimaging databases has been proposed as a promising tool for improving our understanding of the behavioural emergence and early prediction of the neurodevelopmental outcome. A recent example of this strategy is a study by Ouyang et al. (2020) which suggested that cortical microstructure quantified by diffusion MRI through fractional anisotropy (FA) metric in preterm and full-term neonates can lead to effective prediction of language and cognitive outcomes at 2 years of corrected age as assessed by Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) composite scores. Given the important need for robust and generalisable tools which can reliably predict the neurodevelopmental outcome of preterm infants, we aimed to replicate the conclusions of this work using a larger independent dataset from the developing Human Connectome Project dataset (dHCP, third release) with early MRI and BSID-III evaluation at 18 months of corrected age. We then aimed to extend the validation of the proposed predictive pipeline through the study of different cohorts (the largest one included 295 neonates, with gestational age between 29 and 42 week and post-menstrual age at MRI between 31 and 45 weeks). This allowed us to evaluate whether some limitations of the original study (mainly small sample size and limited variability in the input and output features used in the predictive models) would influence the prediction results. In contrast to the original study that inspired the current work, our prediction results did not outcompete the random levels. Furthermore, these negative results persisted even when the study settings were expanded. Our findings suggest that the cortical microstructure close to birth described by DTI-FA measures might not be sufficient for a reliable prediction of BSID-III scores during toddlerhood, at least in the current setting, i.e. generally older cohorts and a different processing pipeline. Our inability to conceptually replicate the results of the original study is in line with the previously reported replicability issues within the machine learning field and demonstrates the challenges in defining the good set of practices for the implementation and validation of reliable predictive tools in the neurodevelopmental (and other) fields.