Neuroimage-Clinical最新文献

Background

Hierarchy is the organizing principle of human brain network. How network hierarchy changes in subthreshold depression (StD) is unclear. The aim of this study was to investigate the altered brain network hierarchy and its clinical significance in patients with StD.

Methods

A total of 43 patients with StD and 43 healthy controls matched for age, gender and years of education participated in this study. Alterations in the hierarchy of StD brain networks were depicted by connectome gradient analysis. We assessed changes in network hierarchy by comparing gradient scores in each network in patients with StD and healthy controls. The study compared different brain subdivisions if there was a different network. Finally, we analysed the relationship between the altered gradient scores and clinical characteristics.

Results

Patients with StD had contracted network hierarchy and suppressed cortical range gradients. In the principal gradient, the gradient scores of default mode network were significantly reduced in patients with StD compared to controls. In the default network, the subdivisions of reduced gradient scores were mainly located in the precuneus, superior temporal gyrus, and anterior and posterior cingulate gyrus. Reduced gradient scores in the default mode network, the anterior and posterior cingulate gyrus were correlated with severity of depression.

Conclusions

The network hierarchy of the StD changed and was significantly correlated with depressive symptoms and severity. These results provided new insights into further understanding of the neural mechanisms of StD.

Background

Strokes frequently result in long-term motor deficits, imposing significant personal and economic burdens. However, our understanding of the underlying neural mechanisms governing motor learning in stroke survivors remains limited - a fact that poses significant challenges to the development and optimisation of therapeutic strategies.

Objective

This study investigates the diversity in motor learning aptitude and its associated neurological mechanisms. We hypothesised that stroke patients exhibit compromised overall motor learning capacity, which is associated with altered activity and connectivity patterns in the motor- and default-mode-network in the brain.

Methods

We assessed a cohort of 40 chronic-stage, mildly impaired stroke survivors and 39 age-matched healthy controls using functional Magnetic Resonance Imaging (fMRI) and connectivity analyses. We focused on neural activity and connectivity patterns during an unilateral motor sequence learning task performed with the unimpaired or non-dominant hand. Primary outcome measures included task-induced changes in neural activity and network connectivity.

Results

Compared to controls, stroke patients showed significantly reduced motor learning capacity, associated with diminished cerebral lateralization. Task induced activity modulation was reduced in the motor network but increased in the default mode network. The modulated activation strength was associated with an opposing trend in task-induced functional connectivity, with increased connectivity in the motor network and decreased connectivity in the DMN.

Conclusions

Stroke patients demonstrate altered neural activity and connectivity patterns during motor learning with their unaffected hand, potentially contributing to globally impaired motor learning skills. The reduced ability to lateralize cerebral activation, along with the enhanced connectivity between the right and left motor cortices in these patients, may signify maladaptive neural processes that impede motor adaptation, possibly affecting long-term rehabilitation post-stroke. The contrasting pattern of activity modulation and connectivity alteration in the default mode network suggests a nuanced role of this network in post-stroke motor learning. These insights could have significant implications for the development of customised rehabilitation strategies for stroke patients.

Degeneration in the substantia nigra (SN) pars compacta (SNc) underlies motor symptoms in Parkinson’s disease (PD). Currently, there are no neuroimaging biomarkers that are sufficiently sensitive, specific, reproducible, and accessible for routine diagnosis or staging of PD. Although iron is essential for cellular processes, it also mediates neurodegeneration. MRI can localize and quantify brain iron using magnetic susceptibility, which could potentially provide biomarkers of PD.

We measured iron in the SNc, SN pars reticulata (SNr), total SN, and ventral tegmental area (VTA), using quantitative susceptibility mapping (QSM) and R2* relaxometry, in PD patients and age-matched healthy controls (HCs). PD patients, diagnosed within five years of participation and HCs were scanned at 3T (22 PD and 23 HCs) and 7T (17 PD and 21 HCs) MRI. Midbrain nuclei were segmented using a probabilistic subcortical atlas. QSM and R2* values were measured in midbrain subregions. For each measure, groups were contrasted, with Age and Sex as covariates, and receiver operating characteristic (ROC) curve analyses were performed with repeated k-fold cross-validation to test the potential of our measures to classify PD patients and HCs. Statistical differences of area under the curves (AUCs) were compared using the Hanley-MacNeil method (QSM versus R2*; 3T versus 7T MRI).

PD patients had higher QSM values in the SNc at both 3T (p_adj = 0.001) and 7T (p_adj = 0.01), but not in SNr, total SN, or VTA, at either field strength. No significant group differences were revealed using R2* in any midbrain region at 3T, though increased R2* values in SNc at 7T MRI were marginally significant in PDs compared to HCs (p_adj = 0.052). ROC curve analyses showed that SNc iron measured with QSM, distinguished early PD patients from HCs at the single-subject level with good diagnostic accuracy, using 3T (mean AUC = 0.83, 95 % CI = 0.82–0.84) and 7T (mean AUC = 0.80, 95 % CI = 0.79–0.81) MRI. Mean AUCs reported here are from averages of tests in the hold-out fold of cross-validated samples. The Hanley-MacNeil method demonstrated that QSM outperforms R2* in discriminating PD patients from HCs at 3T, but not 7T. There were no significant differences between 3T and 7T in diagnostic accuracy of QSM values in SNc.

This study highlights the importance of segmenting midbrain subregions, performed here using a standardized atlas, and demonstrates high accuracy of SNc iron measured with QSM at 3T MRI in identifying early PD patients. QSM measures of SNc show potential for inclusion in neuroimaging diagnostic biomarkers of early PD. An MRI diagnostic biomarker of PD would represent a significant clinical advance.

Despite the prevalence of Parkinson’s disease (PD), there are no clinically-accepted neuroimaging biomarkers to predict the trajectory of motor or cognitive decline or differentiate Parkinson’s disease from atypical progressive parkinsonian diseases. Since abnormal connectivity in the motor circuit and basal ganglia have been previously shown as early markers of neurodegeneration, we hypothesize that patterns of interregional connectivity could be useful to form patient-specific predictive models of disease state and of PD progression. We use fMRI data from subjects with Multiple System Atrophy (MSA), Progressive Supranuclear Palsy (PSP), idiopathic PD, and healthy controls to construct predictive models for motor and cognitive decline and differentiate between the four subgroups. Further, we identify the specific connections most informative for progression and diagnosis. When predicting the one-year progression in the MDS-UPDRS-III^1* and Montreal Cognitive assessment (MoCA), we achieve new state-of-the-art mean absolute error performance. Additionally, the balanced accuracy we achieve in the diagnosis of PD, MSA, PSP, versus healthy controls surpasses that attained in most clinics, underscoring the relevance of the brain connectivity features. Our models reveal the connectivity between deep nuclei, motor regions, and the thalamus as the most important for prediction. Collectively these results demonstrate the potential of fMRI connectivity as a prognostic biomarker for PD and increase our understanding of this disease.

Background

Functional Magnetic Resonance Imaging (fMRI) has shown brain activity alterations in individuals with a history of attempted suicide (SA) who are diagnosed with depression disorder (DD) or bipolar disorder (BD). However, patterns of spontaneous brain activity and their genetic correlations need further investigation.

Methods

A voxel-based meta-analysis of 19 studies including 26 datasets, involving 742 patients with a history of SA and 978 controls (both nonsuicidal patients and healthy controls) was conducted. We examined fMRI changes in SA patients and analyzed the association between these changes and gene expression profiles using data from the Allen Human Brain Atlas by partial least squares regression analysis.

Results

SA patients demonstrated increased spontaneous brain activity in several brain regions including the bilateral inferior temporal gyrus, hippocampus, fusiform gyrus, and right insula, and decreased activity in areas like the bilateral paracentral lobule and inferior frontal gyrus. Additionally, 5,077 genes were identified, exhibiting expression patterns associated with SA-related fMRI alterations. Functional enrichment analyses demonstrated that these SA-related genes were enriched for biological functions including glutamatergic synapse and mitochondrial structure. Concurrently, specific expression analyses showed that these genes were specifically expressed in the brain tissue, in neurons cells, and during early developmental periods.

Conclusion

Our findings suggest a neurobiological basis for fMRI abnormalities in SA patients with DD or BD, potentially guiding future genetic and therapeutic research.

Background

In Huntington’s disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor assessment scores. They focus less on in vivo brain imaging markers, which reflect neuropathology well before clinical diagnosis. Machine learning methods offer a degree of sophistication which could significantly improve prognosis and stratification by leveraging multimodal biomarkers from large datasets. Such models specifically tailored to HD gene expansion carriers could further enhance the efficacy of the stratification process.

Objectives

To improve stratification of Huntington’s disease individuals for clinical trials.

Methods

We used data from 451 gene positive individuals with Huntington’s disease (both premanifest and diagnosed) from previously published cohorts (PREDICT, TRACK, TrackON, and IMAGE). We applied whole-brain parcellation to longitudinal brain scans and measured the rate of lateral ventricular enlargement, over 3 years, which was used as the target variable for our prognostic random forest regression models. The models were trained on various combinations of features at baseline, including genetic load, cognitive and motor assessment score biomarkers, as well as brain imaging-derived features. Furthermore, a simplified stratification model was developed to classify individuals into two homogenous groups (low risk and high risk) based on their anticipated rate of ventricular enlargement.

Results

The predictive accuracy of the prognostic models substantially improved by integrating brain imaging features alongside genetic load, cognitive and motor biomarkers: a 24 % reduction in the cross-validated mean absolute error, yielding an error of 530 mm³/year. The stratification model had a cross-validated accuracy of 81 % in differentiating between moderate and fast progressors (precision = 83 %, recall = 80 %).

Conclusions

This study validated the effectiveness of machine learning in differentiating between low- and high-risk individuals based on the rate of ventricular enlargement. The models were exclusively trained using features from HD individuals, which offers a more disease-specific, simplified, and accurate approach for prognostic enrichment compared to relying on features extracted from healthy control groups, as done in previous studies. The proposed method has the potential to enhance clinical utility by: i) enabling more targeted recruitment of individuals for clinical trials, ii) improving post-hoc evaluation of individuals, and iii) ultimately leading to better outcomes for individuals through personalized treatment selection.

Background

Functional neurological disorder (FND) is a common neuropsychiatric condition with established diagnostic criteria and effective treatments but for which the underlying neuropathophysiological mechanisms remain incompletely understood. Recent neuroimaging studies have revealed FND as a multi-network brain disorder, unveiling alterations across limbic, self-agency, attentional/salience, and sensorimotor networks. However, the relationship between identified brain alterations and disease progression or improvement is less explored.

Methods

This study included resting-state functional magnetic resonance imaging (fMRI) data from 79 patients with FND and 74 age and sex-matched healthy controls (HC). First, voxel-wise BOLD signal variability was computed for each participant and the group-wise difference was calculated. Second, we investigated the potential of BOLD signal variability to serve as a prognostic biomarker for clinical outcome in 47 patients who attended a follow-up measurement after eight months.

Results

The results demonstrated higher BOLD signal variability in key networks, including the somatomotor, salience, limbic, and dorsal attention networks, in patients compared to controls. Longitudinal analysis revealed an increase in BOLD signal variability in the supplementary motor area (SMA) in FND patients who had an improved clinical outcome, suggesting SMA variability as a potential state biomarker. Additionally, higher BOLD signal variability in the left insula at baseline predicted a worse clinical outcome.

Conclusion

This study contributes to the understanding of FND pathophysiology, emphasizing the dynamic nature of neural activity and highlighting the potential of BOLD signal variability as a valuable research tool. The insula and SMA emerge as promising regions for further investigation as prognostic and state markers.

Background

The effects of low-moderate prenatal alcohol exposure (PAE) on brain development have been infrequently studied.

Aim

To compare cortical and white matter structure between children aged 6 to 8 years with low-moderate PAE in trimester 1 only, low-moderate PAE throughout gestation, or no PAE.

Methods

Women reported quantity and frequency of alcohol consumption before and during pregnancy. Magnetic resonance imaging was undertaken for 143 children aged 6 to 8 years with PAE during trimester 1 only (n = 44), PAE throughout gestation (n = 58), and no PAE (n = 41). T₁-weighted images were processed using FreeSurfer, obtaining brain volume, area, and thickness of 34 cortical regions per hemisphere. Fibre density (FD), fibre cross-section (FC) and fibre density and cross-section (FDC) metrics were computed for diffusion images. Brain measures were compared between PAE groups adjusted for age and sex, then additionally for intracranial volume.

Results

After adjustments, the right caudal anterior cingulate cortex volume (p_FDR = 0.045) and area (p_FDR = 0.008), and right cingulum tract cross-sectional area (p_FWE < 0.05) were smaller in children exposed to alcohol throughout gestation compared with no PAE.

Conclusion

This study reports a relationship between low-moderate PAE throughout gestation and cingulate cortex and cingulum tract alterations, suggesting a teratogenic vulnerability. Further investigation is warranted.

Right hemisphere stroke patients frequently present with a combination of lateralised and non-lateralised attentional deficits characteristic of the neglect syndrome. Attentional deficits are associated with poor functional outcome and are challenging to treat, with non-lateralised deficits often persisting into the chronic stage and representing a common complaint among patients and families.

In this study, we investigated the effects of non-invasive brain stimulation on non-lateralised attentional deficits in right-hemispheric stroke. In a randomised double-blind sham-controlled crossover study, twenty-two patients received real and sham transcranial Direct Current Stimulation (tDCS) whilst performing a non-lateralised attentional task. A high definition tDCS montage guided by stimulation modelling was employed to maximise current delivery over the right dorsolateral prefrontal cortex, a key node in the vigilance network. In a parallel study, we examined brain network response to this tDCS montage by carrying out concurrent fMRI during stimulation in healthy participants and patients.

At the group level, stimulation improved target detection in patients, reducing overall error rate when compared with sham stimulation. TDCS boosted performance throughout the duration of the task, with its effects briefly outlasting stimulation cessation. Exploratory lesion analysis indicated that response to stimulation was related to lesion location rather than volume. In particular, reduced stimulation response was associated with damage to the thalamus and postcentral gyrus. Concurrent stimulation-fMRI revealed that tDCS did not affect local connectivity but influenced functional connectivity within large-scale networks in the contralesional hemisphere.

This combined behavioural and functional imaging approach shows that brain stimulation targeted to surviving tissue in the ipsilesional hemisphere improves non-lateralised attentional deficits following stroke. This effect may be exerted via contralesional network effects.

Alzheimer’s disease (AD) and its related age at onset (AAO) are highly heterogeneous, due to the inherent complexity of the disease. They are affected by multiple factors, such as neuroimaging and genetic predisposition. Multimodal integration of various data types is necessary; however, it has been nontrivial due to the high dimensionality of each modality. We aimed to identify multimodal biomarkers of AAO in AD using an extended version of sparse canonical correlation analysis, in which we integrated two imaging modalities, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), and genetic data in the form of single-nucleotide polymorphisms (SNPs) obtained from the Alzheimer’s disease neuroimaging initiative database. These three modalities cover low-to-high-level complementary information and offer multiscale insights into the AAO. We identified multivariate markers of AAO in AD using fMRI, PET, and SNP. Furthermore, the markers identified were largely consistent with those reported in the existing literature. In particular, our serial mediation analysis suggests that genetic variants influence the AAO in AD by indirectly affecting brain connectivity by mediation of amyloid-beta protein accumulation, supporting a plausible path in existing research. Our approach provides comprehensive biomarkers related to AAO in AD and offers novel multimodal insights into AD.