Neuroimage. Reports最新文献

Sex differences in the size of specific brain structures have been extensively studied, but careful and reproducible statistical hypothesis testing to identify them produced overall small effect sizes and differences in brains of males and females. On the other hand, multivariate statistical or machine learning methods that analyze MR images of the whole brain have reported respectable accuracies for the task of distinguishing brains of males from brains of females. However, most existing studies lacked a careful control for brain volume differences between sexes and, if done, their accuracy often declined to 70% or below. This raises questions about the relevance of accuracies achieved without careful control of overall volume.

We examined how accurately sex can be classified from gray matter properties of the human brain when matching on overall brain volume. We tested, how robust machine learning classifiers are when predicting cross-cohort, i.e. when they are used on a different cohort than they were trained on. Furthermore, we studied how their accuracy depends on the size of the training set and attempted to identify brain regions relevant for successful classification. MRI data was used from two population-based data sets of 3298 mostly older adults from the Study of Health in Pomerania (SHIP) and 399 mostly younger adults from the Human Connectome Project (HCP), respectively. We benchmarked two multivariate methods, logistic regression and a 3D convolutional neural network.

We show that male and female brains of the same intracranial volume can be distinguished with >92% accuracy with logistic regression on a dataset of 1166 matched individuals. The same model also reached 85% accuracy on a different cohort without retraining. The accuracy for both methods increased with the training cohort size up to and beyond 3000 individuals, suggesting that classifiers trained on smaller cohorts likely have an accuracy disadvantage. We found no single outstanding brain region necessary for successful classification, but important features appear rather distributed across the brain.

As direct evaluation of a mouse model of human neurodevelopment, adolescent and young adult mice and humans underwent MR diffusion tensor imaging to quantify age-related differences in microstructural integrity of brain white matter fibers. Fractional anisotropy (FA) was greater in older than younger mice and humans. Despite the cross-species commonality, the underlying developmental mechanism differed: whereas evidence for greater axonal extension contributed to higher FA in older mice, evidence for continuing myelination contributed to higher FA in human adolescent development. These differences occurred in the context of species distinctions in overall brain growth: whereas the continued growth of the brain and skull in the murine model can accommodate volume expansion into adulthood, human white matter volume and myelination continue growth into adulthood within a fixed intracranial volume. Appreciation of the similarities and differences in developmental mechanism can enhance the utility of animal models of brain white matter structure, function, and response to exogenous manipulation.

The representation of upper limb muscles in the motor cortex is not clear-cut. The motor cortex contains areas that, when stimulated, may activate different muscles simultaneously, hence they seem to overlap. We expected the cortical representations of synergistic muscle pairs to overlap more than those of non-synergistic muscles. To test this, we used navigated transcranial magnetic stimulation to probe eight hand and forearm muscles of twenty healthy participants. We transformed the cortical representations of muscles to a template MRI to allow for group analysis. We found that the amount of overlap in cortical representations differed significantly between within-hand and within-forearm muscle combinations. Most synergistic muscle pairs, both within the hand, within the forearm and between them, had a larger overlap than non-synergistic muscle pairs. Our study demonstrates the largely overlapping nature of cortical representations of upper limb muscles. It is noteworthy that the overlap is elevated in muscles that usually act in a synergistic manner.

Defensive stress reactions, such as freezing and active fight-or-flight, are relevant for coping with threat. Action-preparatory activity supporting these reactions, including the amygdala, has been posited as a potential marker for stress-resilience. We considered the successive COVID-19 lockdowns as two pervasive stressors, to prospectively investigate the predictive value of neural threat-responses towards symptom development. Five years prior to the COVID-19 pandemic, 17-year-old adolescents (n = 64, Baseline-17) performed the fMRI-adapted Go/Nogo Under Threat (GUNT) task, where threat-anticipatory freezing reactions and transition to action are evoked to avoid a shock. A majority (n = 44) made themselves available for follow-up assessments before COVID (Baseline-20, age 20), during the first COVID-19 lockdown in the Netherlands (LD1, age 22.5), and during a second lockdown (LD2, age 23). The GUNT task quantified neural (thalamic, subcortical, amygdala) and physiological (bradycardia) markers of threat-anticipatory freezing and transition to action (mediated by anterior cingulate cortex). Threat-anticipatory amygdala responses (Baseline-17) were linked to stressor resilience, as quantified by self-reported anxiety symptoms between LD1 and LD2. However, stronger amygdala responses to low threat cues (Baseline-17) were associated with stronger anxiety symptoms. These effects occurred over and above early-life stress, COVID-19 stress burden, and overall symptom changes between age 17 and 20. These findings suggest that amygdala responses to acute threat provide a marker for resilience against real-life stressors, with adequate threat discrimination signaling resilience and stronger amygdala responses to low threat predicting vulnerability. The findings support the notion that neural responses to threat are instrumental for adaptive coping with pervasive stress.

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.