Neuroimage-Clinical最新文献

Neuroimaging studies have indicated widespread brain structural and functional disruptions in patients with obsessive–compulsive disorder (OCD). However, the underlying mechanism of these changes remains unclear. A total of 45 patients with OCD and 42 healthy controls (HC) were enrolled. The study investigated local degree centrality (DC) abnormalities and employed abnormal regions of DC as seeds to investigate variability in dynamic functional connectivity (dFC) in the whole brain using a sliding window approach to analyze resting-state functional magnetic resonance imaging. The relationship between abnormal DC and dFC as well as the clinical features of OCD were examined using correlation analysis. Our findings suggested decreased DC in the bilateral thalamus, bilateral precuneus, and bilateral cuneus in OCD patients and a nominally negative correlation between the DC value in the thalamus and illness severity measured using the Yale-Brown Obsessive Compulsive Scale (Y-BOCS). In addition, seed-based dFC analysis showed that compared to measurements in the HC, the patients had decreased dFC variability between the left thalamus and the left cuneus and right lingual gyrus, and between the bilateral cuneus and bilateral postcentral gyrus, and a nominally positive correlation between the duration of illness and dFC variability between the left cuneus and left postcentral gyrus. These results indicated that OCD patients had decreased hub importance in the bilateral thalamus and cuneus throughout the entire brain. This reduction was associated with impaired coupling with dynamic function in the visual cortex and sensorimotor network and provided novel insights into the neurophysiological mechanisms underlying OCD.

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

Objectives

In vivo magnetic resonance spectroscopy (MRS) was used to investigate neurometabolic homeostasis in children with functional neurological disorder (FND) in three regions of interest: supplementary motor area (SMA), anterior default mode network (aDMN), and posterior default mode network (dDMN). Metabolites assessed included N-acetyl aspartate (NAA), a marker of neuron function; myo-inositol (mI), a glial-cell marker; choline (Cho), a membrane marker; glutamate plus glutamine (Glx), a marker of excitatory neurotransmission; γ-aminobutyric acid (GABA), a marker of inhibitor neurotransmission; and creatine (Cr), an energy marker. The relationship between excitatory (glutamate and glutamine) and inhibitory (GABA) neurotransmitter (E/I) balance was also examined.

Methods

MRS data were acquired for 32 children with mixed FND (25 girls, 7 boys, aged 10.00 to 16.08 years) and 41 healthy controls of similar age using both short echo point-resolved spectroscopy (PRESS) and Mescher-Garwood point-resolved spectroscopy (MEGAPRESS) sequences in the three regions of interest.

Results

In the SMA, children with FND had lower NAA/Cr, mI/Cr (trend level), and GABA/Cr ratios. In the aDMN, no group differences in metabolite ratios were found. In the pDMN, children with FND had lower NAA/Cr and mI/Cr (trend level) ratios. While no group differences in E/I balance were found (FND vs. controls), E/I balance in the aDMN was lower in children with functional seizures—a subgroup within the FND group. Pearson correlations found that increased arousal (indexed by higher heart rate) was associated with lower mI/Cr in the SMA and pDMN.

Conclusions

Our findings of multiple differences in neurometabolites in children with FND suggest dysfunction on multiple levels of the biological system: the neuron (lower NAA), the glial cell (lower mI), and inhibitory neurotransmission (lower GABA), as well as dysfunction in energy regulation in the subgroup with functional seizures.

Objective

In drug-resistant temporal lobe epilepsy (TLE), it is not well-established in how far surgery should target morphological anomalies to achieve seizure freedom. Here, we assessed interactions between structural brain compromise and surgery to identify region-specific predictors of seizure outcome.

Methods

We obtained pre- and post-operative 3D T1-weighted MRI in 55 TLE patients who underwent selective amygdalo-hippocampectomy (SAH) or anterior temporal lobectomy (ATL) and 40 age and sex-matched healthy subjects. We measured surface-based morphological alterations of the mesiotemporal lobe structures (hippocampus, amygdala, entorhinal and piriform cortices), the neocortex and the thalamus on both pre- and post-operative MRI. Using precise co-registration, in each patient we mapped the surgical cavity onto the MRI acquired before surgery, thereby quantifying the amount of pathological tissue resected; these features, together with the preoperative morphometric data, served as input to a supervised classification algorithm for postsurgical outcome prediction.

Results

On pre-operative MRI, patients who became seizure-free (TLE-SF) presented with severe ipsilateral amygdalar and hippocampal atrophy, while not seizure-free patients (TLE-NSF) displayed amygdalar hypertrophy. Stratifying patients based on the surgical approach, post-operative MRI showed similar patterns of mesiotemporal and thalamic changes, but divergent neocortical thinning affecting the parieto-temporo-occipital regions following ATL and the frontal lobes after SAH. Irrespective of the surgical approach, hippocampal atrophy on pre-operative MRI and its extent of resection were the most predictive features of seizure-freedom in 89% of patients (selected 100% across validations).

Significance

Our study indicates a critical role of the extent of resection of MRI-derived hippocampal morphological anomalies on seizure outcome. Precise pre-operative quantification of the mesiotemporal lobe provides non-invasive prognostics for individualized surgery.

Background and objectives

The intricate relationship between deep brain stimulation (DBS) in Parkinson’s disease (PD) and cognitive impairment has lately garnered substantial attention. The presented study evaluated pre-DBS structural and microstructural cerebral patterns as possible predictors of future cognitive decline in PD DBS patients.

Methods

Pre-DBS MRI data in 72 PD patients were combined with neuropsychological examinations and follow-up for an average of 2.3 years after DBS implantation procedure using a screening cognitive test validated for diagnosis of mild cognitive impairment in PD in a Czech population – Dementia Rating Scale 2.

Results

PD patients who would exhibit post-DBS cognitive decline were found to have, already at the pre-DBS stage, significantly lower cortical thickness and lower microstructural complexity than cognitively stable PD patients. Differences in the regions directly related to cognition as bilateral parietal, insular and cingulate cortices, but also occipital and sensorimotor cortex were detected. Furthermore, hippocampi, putamina, cerebellum and upper brainstem were implicated as well, all despite the absence of pre-DBS differences in cognitive performance and in the position of DBS leads or stimulation parameters between the two groups.

Conclusions

Our findings indicate that the cognitive decline in the presented PD cohort was not attributable primarily to DBS of the subthalamic nucleus but was associated with a clinically silent structural and microstructural predisposition to future cognitive deterioration present already before the DBS system implantation.

Machine learning offers great potential for automated prediction of post-stroke symptoms and their response to rehabilitation. Major challenges for this endeavour include the very high dimensionality of neuroimaging data, the relatively small size of the datasets available for learning and interpreting the predictive features, as well as, how to effectively combine neuroimaging and tabular data (e.g. demographic information and clinical characteristics). This paper evaluates several solutions based on two strategies. The first is to use 2D images that summarise MRI scans. The second is to select key features that improve classification accuracy. Additionally, we introduce the novel approach of training a convolutional neural network (CNN) on images that combine regions-of-interests (ROIs) extracted from MRIs, with symbolic representations of tabular data.

We evaluate a series of CNN architectures (both 2D and a 3D) that are trained on different representations of MRI and tabular data, to predict whether a composite measure of post-stroke spoken picture description ability is in the aphasic or non-aphasic range. MRI and tabular data were acquired from 758 English speaking stroke survivors who participated in the PLORAS study. Each participant was assigned to one of five different groups that were matched for initial severity of symptoms, recovery time, left lesion size and the months or years post-stroke that spoken description scores were collected. Training and validation were carried out on the first four groups. The fifth (lock-box/test set) group was used to test how well model accuracy generalises to new (unseen) data.

The classification accuracy for a baseline logistic regression was 0.678 based on lesion size alone, rising to 0.757 and 0.813 when initial symptom severity and recovery time were successively added. The highest classification accuracy (0.854), area under the curve (0.899) and F1 score (0.901) were observed when 8 regions of interest were extracted from each MRI scan and combined with lesion size, initial severity and recovery time in a 2D Residual Neural Network (ResNet). This was also the best model when data were limited to the 286 participants with moderate or severe initial aphasia (with area under curve = 0.865), a group that would be considered more difficult to classify.

Our findings demonstrate how imaging and tabular data can be combined to achieve high post-stroke classification accuracy, even when the dataset is small in machine learning terms. We conclude by proposing how the current models could be improved to achieve even higher levels of accuracy using images from hospital scanners.

The microbiome-gut-brain axis (MGBA) plays a critical role in schizophrenia (SZ). However, the underlying mechanisms of the interactions among the gut microbiome, brain networks, and symptom severity in SZ patients remain largely unknown. Fecal samples, structural and functional magnetic resonance imaging (MRI) data, and Positive and Negative Syndrome Scale (PANSS) scores were collected from 38 SZ patients and 38 normal controls, respectively. The data of 16S rRNA gene sequencing were used to analyze the abundance of gut microbiome and the analysis of human brain networks was applied to compute the nodal properties of 90 brain regions. A total of 1,691,280 mediation models were constructed based on 261 gut bacterial, 810 nodal properties, and 4 PANSS scores in SZ patients. A strong correlation between the gut microbiome and brain networks (r = 0.89, false discovery rate (FDR) -corrected p < 0.05) was identified. Importantly, the PANSS scores were linearly correlated with both the gut microbiome (r = 0.5, FDR-corrected p < 0.05) and brain networks (r = 0.59, FDR-corrected p < 0.05). The abundance of genus Sellimonas significantly affected the PANSS negative scores of SZ patients via the betweenness centrality of white matter networks in the inferior frontal gyrus and amygdala. Moreover, 19 significant mediation models demonstrated that the nodal properties of 7 brain regions, predominately from the systems of visual, language, and control of action, showed significant mediating effects on the PANSS scores with the gut microbiome as mediators. Together, our findings indicated the tripartite relationships among the gut microbiome, brain networks, and PANSS scores and suggested their potential role in the neuropathology of SZ.

Background

Volumetric investigations of cortical damage resulting from stroke indicate that lesion size and shape continue to change even in the chronic stage of recovery. However, the potential clinical relevance of continued lesion growth has yet to be examined. In the present study, we investigated the prevalence of lesion expansion and the relationship between expansion and changes in aphasia severity in a large sample of individuals in the chronic stage of aphasia recovery.

Methods

Retrospective structural MRI scans from 104 S survivors with at least 2 observations (k = 301 observations; mean time between scans = 31 months) were included. Lesion demarcation was performed using an automated lesion segmentation software and lesion volumes at each timepoint were subsequently calculated. A linear mixed effects model was conducted to investigate the effect of days between scan on lesion expansion.

Finally, we investigated the association between lesion expansion and changes on the Western Aphasia Battery (WAB) in a group of participants assessed and scanned at 2 timepoints (N = 54) using a GLM.

Results

Most participants (81 %) showed evidence of lesion expansion. The mixed effects model revealed lesion volumes significantly increase, on average, by 0.02 cc each day (7.3 cc per year) following a scan (p < 0.0001).

Change on language performance was significantly associated with change in lesion volume (p = 0.025) and age at stroke (p = 0.031). The results suggest that with every 10 cc increase in lesion size, language performance decreases by 0.9 points, and for every 10-year increase in age at stroke, language performance decreases by 1.9 points.

Conclusions

The present study confirms and extends prior reports that lesion expansion occurs well into the chronic stage of stroke. For the first time, we present evidence that expansion is predictive of longitudinal changes in language performance in individuals with aphasia. Future research should focus on the potential mechanisms that may lead to necrosis in areas surrounding the chronic stroke lesion.

Introduction

The dynamics of large-scale networks, which are known as distributed sets of functionally synchronized brain regions and include the visual network (VIN), somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network (FPN), and default mode network (DMN), play important roles in emotional and cognitive processes in humans. Although disruptions in these large-scale networks are considered critical for the pathophysiological mechanisms of psychiatric disorders, their role in psychiatric disorders remains unknown. We aimed to elucidate the aberrant dynamics across large-scale networks in patients with schizophrenia (SZ) and mood disorders.

Methods

We performed energy-landscape analysis to investigate the aberrant brain dynamics of seven large-scale networks across 50 healthy controls (HCs), 36 patients with SZ, and 42 patients with major depressive disorder (MDD) recruited at Wakayama Medical University. We identified major patterns of brain activity using energy-landscape analysis and estimated their duration, occurrence, and ease of transition.

Results

We identified four major brain activity patterns that were characterized by the activation patterns of the DMN and VIN (state 1, DMN (-) VIN (-); state 2, DMN (+) VIN (+); state 3, DMN (-) VIN (+); and state 4, DMN (+) VIN (-)). The duration of state 1 and the occurrence of states 1 and 2 were shorter in the SZ group than in HCs and the MDD group, and the duration of state 3 was longer in the SZ group. The ease of transition between states 3 and 4 was larger in the SZ group than in the HCs and the MDD group. The ease of transition from state 3 to state 4 was negatively associated with verbal fluency in patients with SZ. The current study showed that the brain dynamics was more disrupted in SZ than in MDD.

Conclusions

Energy-landscape analysis revealed aberrant brain dynamics across large-scale networks between SZ and MDD and their associations with cognitive abilities in SZ, which cannot be captured by conventional functional connectivity analyses. These results provide new insights into the pathophysiological mechanisms underlying SZ and mood disorders.

Background

MR-guided focused ultrasound (MRgFUS) thalamotomy is a novel and effective treatment for medication-refractory tremor in essential tremor (ET), but how the brain responds to this deliberate lesion is not clear.

Objective

The current study aimed to evaluate the immediate and longitudinal alterations of functional networks after MRgFUS thalamotomy.

Methods

We retrospectively obtained preoperative and postoperative 30-day, 90-day, and 180-day data of 31 ET patients subjected with MRgFUS thalamotomy from 2018 to 2020. Their archived resting-state functional MRI data were used for functional network comparison as well as graph-theory metrics analysis. Both partial least squares (PLS) regression and linear regression were conducted to associate functional features to tremor symptoms.

Results

MRgFUS thalamotomy dramatically abolished tremors, while global functional network only sustained immediate fluctuation within one week after the surgery. Network-based statistics have identified a long-term enhanced corticostriatal subnetwork by comparison between 180-day and preoperative data (P = 0.019). Within this subnetwork, network degree, global efficiency and transitivity were significantly recovered in ET patients right after MRgFUS thalamotomy compared to the pre-operative timepoint (P < 0.05), as well as hemisphere lateralization (P < 0.001). The PLS main component significantly accounted for 33.68 % and 34.16 % of the total variances of hand tremor score and clinical rating scale for tremor (CRST)-total score (P = 0.037 and 0.027). Network transitivity of this subnetwork could serve as a reliable biomarker for hand tremor score control prediction at 180-day after the surgery (β = 2.94, P = 0.03).

Conclusion

MRgFUS thalamotomy promoted corticostriatal connectivity activation correlated with tremor improvement in ET patient after MRgFUS thalamotomy.