Functional Magnetic Resonance Data Research Articles

Ketogenic diet reduces a neurobiological craving signature in inpatients with alcohol use disorder.

Increasing evidence suggests that a ketogenic (high-fat, low-carbohydrate) diet (KD) intervention reduces alcohol withdrawal severity and alcohol craving in individuals with alcohol use disorder (AUD) by shifting brain energetics from glucose to ketones. We hypothesized that the KD would reduce a neurobiological craving signature when individuals undergoing alcohol detoxification treatment were exposed to alcohol cues. We performed a secondary analysis of functional magnetic resonance data of 33 adults with an AUD who were randomized to a KD (n = 19) or a standard American diet (SA; n = 14) and underwent 3 weeks of inpatient alcohol detoxification treatment. Once per week, participants performed an alcohol cue-reactivity paradigm with functional magnetic resonance imaging. We extracted brain responses to food and alcohol cues and quantified the degree to which each set of brain images shared a pattern of activation with a recently established 'Neurobiological Craving Signature' (NCS). We then performed a group-by-time repeated measures ANOVA to test for differences in craving signature expression between the dietary groups over the three-week treatment period. We also correlated these expression patterns with self-reported wanting ratings for alcohol cues. For alcohol relative to food cues, there was a main effect of group, such that the KD group showed lower NCS expression across all 3 weeks of treatment. The main effect of time and the group-by-time interaction were not significant. Self-reported wanting for alcohol cues reduced with KD compared to SA but did not correlate with the NCS score. A ketogenic diet reduces self-reported alcohol wanting, and induced lower NCS to alcohol cues during inpatient treatment for AUD. However, in the KD group alcohol wanting continued to decrease across the 3 weeks of abstinence while the NCS scores remained stable, suggesting that this cue-induced NCS may not fully capture ongoing, non-cue-induced alcohol desire.

Study on brain mechanism of rehabilitation training of articulation disorder in cleft lip and palate patients based on functional magnetic resonance imaging

The cleft lip and palate (CLP) is one of the most common craniofacial malformations in humans. We collected functional magnetic resonance data of 23 CLP patients before rehabilitation training (Bclp) and 23 CLP patients after rehabilitation training (Aclp), who were performing Chinese character pronunciation tasks, and performed brain activation analysis to explore the changes of brain mechanism in CLP patients after articulation disorder rehabilitation training. The study found that Aclp group had significant activation in the motor cortex, Broca area, Wernicke area and cerebellum. While the Bclp group had weak activation in the motor cortex with a small activation range. By comparing the differences and co-activated brain regions between the two groups, we found that rehabilitation training increased the activity level of negatively activated brain areas (cerebellum, left motor area, Wernicke area, etc.) to a positive level. At the same time, the activity level of weakly activated brain areas (right motor area, Broca area, etc.) was also increased. Rehabilitation training promoted the activity level of articulation-related brain regions. So that the activation intensity of articulation-related brain regions can be used as a quantifiable objective evaluation index to evaluate the effect of rehabilitation training, which is of great significance for the formulation of rehabilitation training programs.

Mixture Components Inference for Sparse Regression: Introduction and Application for Estimation of Neuronal Signal from fMRI BOLD

Sparse linear regression methods including the well-known LASSO and the Dantzig selector have become ubiquitous in the engineering practice, including in medical imaging. Among other tasks, they have been successfully applied for the estimation of neuronal activity from functional magnetic resonance data without prior knowledge of the stimulus or activation timing, utilizing an approximate knowledge of the hemodynamic response to local neuronal activity. These methods work by generating a parametric family of solutions with different sparsity, among which an ultimate choice is made using an information criteria. We propose a novel approach, that instead of selecting a single option from the family of regularized solutions, utilizes the whole family of such sparse regression solutions. Namely, their ensemble provides a first approximation of probability of activation at each time-point, and together with the conditional neuronal activity distributions estimated with the theory of mixtures with varying concentrations, they serve as the inputs to a Bayes classifier eventually deciding on the verity of activation at each time-point. We show in extensive numerical simulations that this new method performs favourably in comparison with standard approaches in a range of realistic scenarios. This is mainly due to the avoidance of overfitting and underfitting that commonly plague the solutions based on sparse regression combined with model selection methods, including the corrected Akaike Information Criterion. This advantage is finally documented in selected fMRI task datasets.

SF-MVPA: A from raw data to statistical results and surface space-based MVPA toolbox.

Compared with traditional volume space-based multivariate pattern analysis (MVPA), surface space-based MVPA has many advantages and has received increasing attention. However, surface space-based MVPA requires considerable programming and is therefore difficult for people without a programming foundation. To address this, we developed a MATLAB toolbox based on a graphical interactive interface (GUI) called surface space-based multivariate pattern analysis (SF-MVPA) in this manuscript. Unlike the traditional MVPA toolboxes, which often only include MVPA calculation processes after data preprocessing, SF-MVPA covers the complete pipeline of surface space-based MVPA, including raw data format conversion, surface reconstruction, functional magnetic resonance (fMRI) data preprocessing, comparative analysis, surface space-based MVPA, leave one-run out cross validation, and family-wise error correction. With SF-MVPA, users can complete the complete pipeline of surface space-based MVPA without programming. In addition, SF-MVPA is designed for parallel computing and hence has high computational efficiency. After introducing SF-MVPA, we analyzed a sample dataset of tonal working memory load. By comparison with another surface space-based MVPA toolbox named CoSMoMVPA, we found that the two toolboxes obtained consistent results. We hope that through this toolbox, users can more easily implement surface space-based MVPA.

Complementary hemispheric lateralization of language and social processing in the human brain.

Humans have a unique ability to use language for social communication. The neural architecture for language comprehension and production may have prominently emerged in the brain areas that were originally involved in social cognition. Here, we directly tested the fundamental link between language and social processing using functional magnetic resonance data (MRI) data from over 1,000 human subjects. Cortical activations in language and social tasks showed a striking similarity with a complementary hemispheric lateralization. Within core language areas, left-lateralized activations in the language task were mirrored by right-lateralized activations in the social task. Outside these areas, the activations were left lateralized in both tasks, perhaps indicating multimodal integration of social and semantic information. Our findings could have important implications in understanding neurocognitive mechanisms of social disorders such as autism.

A resting-state fMRI study of temporal lobe epilepsy using multivariate pattern analysis and Granger causality analysis.

Understanding the pathogenesis of temporal lobe epilepsy (TLE) is essential for its diagnosis and treatment. The study aimed to explore regional homogeneity (ReHo) and changes in effective connectivity (EC) between brain regions in TLE patients, hoping to discover potential abnormalities in certain brain regions in TLE patients. Resting-state functional magnetic resonance data were collected from 23 TLE patients and 32 normal controls (NC). ReHo was used as a feature of multivariate pattern analysis (MVPA) to explore the ability of its alterations in identifying TLE. Based on the results of the MVPA, certain brain regions were selected as seed points to further explore alterations in EC between brain regions using Granger causality analysis. MVPA results showed that the classification accuracy for the TLE and NC groups was 87.27%, and the right posterior cerebellum lobe, right lingual gyrus (LING_R), right cuneus (CUN_R), and left superior temporal gyrus (STG_L) provided significant contributions. Moreover, the EC from STG_L to right fusiform gyrus (FFG_R) and LING_R and the EC from CUN_R to the right occipital superior gyrus (SOG_R) and right occipital middle gyrus (MOG_R) were altered compared to the NC group. The MVPA results indicated that ReHo abnormalities in brain regions may be an important feature in the identification of TLE. The enhanced EC from STG_L to FFG_R and LING_R indicates a shift in language processing to the right hemisphere, and the weakened EC from SOG_R and MOG_R to CUN_R may reveal an underlying mechanism of TLE.

Frequency-Specific Analysis of the Dynamic Reconfiguration of the Brain in Patients with Schizophrenia.

The analysis of resting-state fMRI signals usually focuses on the low-frequency range/band (0.01–0.1 Hz), which does not cover all aspects of brain activity. Studies have shown that distinct frequency bands can capture unique fluctuations in brain activity, with high-frequency signals (>0.1 Hz) providing valuable information for the diagnosis of schizophrenia. We hypothesized that it is meaningful to study the dynamic reconfiguration of schizophrenia through different frequencies. Therefore, this study used resting-state functional magnetic resonance (RS-fMRI) data from 42 schizophrenia and 40 normal controls to investigate dynamic network reconfiguration in multiple frequency bands (0.01–0.25 Hz, 0.01–0.027 Hz, 0.027–0.073 Hz, 0.073–0.198 Hz, 0.198–0.25 Hz). Based on the time-varying dynamic network constructed for each frequency band, we compared the dynamic reconfiguration of schizophrenia and normal controls by calculating the recruitment and integration. The experimental results showed that the differences between schizophrenia and normal controls are observed in the full frequency, which is more significant in slow3. In addition, as visual network, attention network, and default mode network differ a lot from each other, they can show a high degree of connectivity, which indicates that the functional network of schizophrenia is affected by the abnormal brain state in these areas. These shreds of evidence provide a new perspective and promote the current understanding of the characteristics of dynamic brain networks in schizophrenia.

Functional Specificity of TPS Brain Stimulation Effects in Patients with Alzheimer's Disease: A Follow-up fMRI Analysis.

IntroductionTranscranial pulse stimulation (TPS) has been recently introduced as a novel clinical brain stimulation technique based on highly focused ultrasound pressure pulses. In a first pilot study on clinical effects of navigated and focused ultrasound neuromodulation, a dichotomy of functional effects was found: patients with Alzheimer’s disease improved cognition and language but deteriorated with visuo-constructive functions.MethodsWe analyzed changes in functional connectivity measured with functional magnetic resonance imaging (fMRI) using graph analysis of a visuo-constructive network in 18 patients with Alzheimer’s disease. We calculated the network’s global efficiency and tested for correlation with visuo-constructive test scores to explain this dichotomy.ResultsImportant visuo-constructive network nodes were not stimulated in the pilot setting and correspondingly global efficiency of a visuo-constructive network was decreased after TPS therapy, compatible with a natural progress of the disease. A correlation between visuo-constructive scores and changes in global efficiency was found.ConclusionResults argue for a high functional specificity of ultrasound-based neuromodulation with TPS.

Diagnosis of Alzheimer's disease by feature weighted-LSTM: a preliminary study of temporal features in brain resting-state fMRI.

The long short-term memory network (LSTM) is widely used in time series data processing as a temporal recursive network. The resting-state functional magnetic resonance data shows that not only are there temporal variations in the resting state, but there are also interactions between brain regions. To integrate the temporal and spatial characteristics of brain regions, this paper proposes a model called feature weighted-LSTM (FW-LSTM). The feature weight is defined by spatial characteristics calculating the frequency of connectivity of each brain region and further integrated into the LSTM. Thus, it can comprehensively model both temporal and spatial changes in rs-fMRI brain regions. The FW-LSTM model on the Alzheimer's disease neuroimaging initiative (ADNI) dataset is used to extract the time-varying characteristics of 90 brain regions for Alzheimer's disease (AD) classification. The model performances are 77.80%, 76.41%, and 78.81% in accuracy, sensitivity, and specificity. It outperformed the one-dimensional convolutional neural networks (1D-CNN) model and LSTM model, which only used temporal features of brain regions.

Individual differences in mental imagery modulate effective connectivity of scene-selective regions during resting state

Successful navigation relies on the ability to identify, perceive, and correctly process the spatial structure of a scene. It is well known that visual mental imagery plays a crucial role in navigation. Indeed, cortical regions encoding navigationally relevant information are also active during mental imagery of navigational scenes. However, it remains unknown whether their intrinsic activity and connectivity reflect the individuals’ ability to imagine a scene. Here, we primarily investigated the intrinsic causal interactions among scene-selective brain regions such as Parahipoccampal Place Area (PPA), Retrosplenial Complex, and Occipital Place Area (OPA) using Dynamic Causal Modelling for resting-state functional magnetic resonance data. Second, we tested whether resting-state effective connectivity parameters among scene-selective regions could reflect individual differences in mental imagery in our sample, as assessed by the self-reported Vividness of Visual Imagery Questionnaire. We found an inhibitory influence of occipito-medial on temporal regions, and an excitatory influence of more anterior on more medial and posterior brain regions. Moreover, we found that a key role in imagery is played by the connection strength from OPA to PPA, especially in the left hemisphere, since the influence of the signal between these scene-selective regions positively correlated with good mental imagery ability. Our investigation contributes to the understanding of the complexity of the causal interaction among brain regions involved in navigation and provides new insight in understanding how an essential ability, such as mental imagery, can be explained by the intrinsic fluctuation of brain signal.

Analysis of Brain Activity Changes in Patients with Parkinson's Disease Based on Resting-State Functional Magnetic Resonance Imaging.

This paper uses resting-state functional magnetic resonance imaging to observe the changes in local consistency of brain activity in patients with Parkinson's disease (PD). Both healthy volunteers and Parkinson's disease patients were scanned for resting brain functional imaging, and the collected raw data were processed using resting functional magnetic resonance data processing toolkit software. This study adopted the use of Regional Homogeneity (ReHo). The postprocessing method of RS-fMRI is to study the spontaneous brain activity changes of patients with Parkinson's disease and cognitive impairment and to explore the changes in the function of their brain regions in the hope of providing help for the treatment of Parkinson's disease cognitive impairment. The results showed that, compared with the normal control group, the brain regions with increased ReHo values in the PD group were the right central anterior gyrus, the right lingual gyrus, the left middle occipital gyrus, and the bilateral anterior cuneiform lobes. The results show that PD patients have abnormal brain nerve activities in the resting state, and these abnormal brain nerve activities may be related to PD cognitive and behavioral dysfunction.

A proposition for analyses and reporting standards for structural and functional magnetic resonance imaging of the noradrenergic locus coeruleus

AbstractBackgroundThe noradrenergic locus coeruleus (LC) in the brainstem shows earliest signs of protein pathologies in neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease. Its small size (about 3‐4mm in width and 15 mm in length) makes it less suitable for Positron Emission Tomography (PET) investigations, so the past decade has seen a steep rise in structural and functional Magnetic Resonance (MR) studies aiming to characterise the LC's changes in ageing and neurodegeneration. However, given its position in the brainstem and small volume, great care has to be taken to yield methodologically reliable MR results as imprecisions in spatial transformations of functional data in the range of 2‐3mm might make LC activations at the group level effectively disappear (Figure 1B). Here, we suggest a post‐processing pipeline and set of quality control protocols which will allow LC researchers to reach the spatial precision necessary for investigating this small but potentially impactful brain structure.MethodUsing a combination of available toolboxes (SPM, ANTs, FSL, FreeSurfer), individual 3T scans for structural and functional LC characterisation are transformed into MNI space via a study‐specific template (Figure 1A). Concatenation of independently optimised transformation steps minimises interpolation applied to the data. Following this, spatial misalignment in individual MNI‐transformed are assessed using Euclidean distance (ED) measures using slice‐specific centres of structural LC masks and distance to landmarks defined based on salient anatomical features of mean functional LC images, respectively.ResultMedian Euclidean Distance of all landmarks on the transformed functional as well as structural LC imaging data were below 2 mm, thereby falling below the typical LC width of 2.5 mm shown in post‐mortem data (Fernandes et al., 2012), or half of the maximum width of the standard‐space LC mask of 4 mm (Keren et al. 2009).ConclusionWith the set of spatial post‐processing steps outlined in the poster and available for download soon, we hope to have taken a first step towards establishing reporting standards of LC imaging data and to give readers interested in LC imaging a starting point for more reliable analyses of structural and functional MR data of the LC.

Abnormal Network Homogeneity in the Right Superior Medial Frontal Gyrus in Cervical Dystonia.

Background: Increasing evidence from modern neuroimaging has confirmed that cervical dystonia (CD) is caused by network abnormalities. Specific brain networks are known to be crucial in patients suffering from CD. However, changes in network homogeneity (NH) in CD patients have not been characterized. Therefore, the purpose of this study was to investigate the NH of patients with CD.Methods: An automated NH method was used to analyze resting-state functional magnetic resonance (fMRI) data from 19 patients with CD and 21 gender- and age-matched healthy controls (HC). Correlation analysis were conducted between NH, illness duration and symptom severity measured by the Tsui scale.Results: Compared with the HC group, CD patients showed a lower NH in the right superior medial frontal gyrus. No significant correlations were found between abnormal NH values and illness duration or symptom severity.Conclusion: Our findings suggest the existence of abnormal NH in the default mode network (DMN) of CD patients, and thereby highlight the importance of the DMN in the pathophysiology of CD.

Abnormal Stability of Dynamic Functional Architecture in Amyotrophic Lateral Sclerosis: A Preliminary Resting-State fMRI Study.

Purpose: Static and dynamic analyses for identifying functional connectivity (FC) have demonstrated brain dysfunctions in amyotrophic lateral sclerosis (ALS). However, few studies on the stability of dynamic FC have been conducted among ALS patients. This study explored the change of functional stability in ALS and how it correlates with disease severity.Methods: We gathered resting-state functional magnetic resonance data from 20 patients with ALS and 22 healthy controls (HCs). The disease severity was assessed with the Revised ALS Functional Rating Scale (ALSFRS-R). We used a sliding window correlation approach to identify dynamic FC and measured the concordance of dynamic FC over time to obtain the functional stability of each voxel. We assessed the between-group difference in functional stability by voxel-wise two-sample t-test. The correlation between the functional stability index and ALSFRS-R in ALS patients was evaluated using Spearman's correlation analysis.Results: Compared with the HC group, the ALS group had significantly increased functional stability in the left pre-central and post-central gyrus and right temporal pole while decreased functional stability in the right middle and inferior frontal gyrus. The results revealed a significant correlation between ALSFRS-R and the mean functional stability in the right temporal pole (r = −0.452 and P = 0.046) in the ALS patients.Conclusions: ALS patients have abnormal stability of brain functional architecture, which is associated with the severity of the disease.

Functional Brain Connectivity Hyper-Network Embedded with Structural Information for Epilepsy Diagnosis

Automatic diagnosis of brain diseases based on brain connectivity network (BCN) classification is one of the hot research fields in medical image analysis. The functional brain network reflects the brain functional activities and structural brain network reflects the neural connections of the main brain regions. It is of great significance to explore and explain the inner mechanism of the brain and to understand and treat brain diseases. In this paper, based on the graph structure characteristics of brain network, the fusion model of functional brain network and structural brain network is designed to classify the diagnosis of brain mental diseases. Specifically, the main work of this paper is to use the Laplacian graph embed the information of diffusion tensor imaging, which contains the characteristics of structural brain networks, into the functional brain network with hyper-order functional connectivity information built based on functional magnetic resonance data using the sparse representation method, to obtain brain network with both functional and structural characteristics. Projection of the brain network and the two original modes data to the kernel space respectively and then classified by the multi-task learning method. Experiments on the epilepsy dataset show that our method has better performance than several state-of-the-art methods. In addition, brain regions and connections that are highly correlated with disease revealed by our method are discussed.

Monitoring of Motor Nerve Function Rehabilitation in Patients with Upper Extremity Hemiplegia Based on Functional Magnetic Resonance Imaging

Through functional magnetic resonance imaging (fMRI) technology, it is planned to use complex brain network technology to track brain functional imaging tracking treatment of stroke hemiplegia with scalp acupuncture. Functional magnetic resonance imaging (fMRI) can continuously monitor the rehabilitation process of motor nerve function in patients with stroke and upper limb hemiplegia, and explore the mechanism of brain plasticity changes at different levels of neural function cortex, motor function neural circuit, and behavior level. First, the fMRI test uses a block design, and the subjects complete the movement of the thumb and index finger. After completing the dysfunction assessment, fMRI data collection was performed on the patient before the CIMT treatment using a magnetic resonance apparatus, and a second fMRI data collection was performed 2 weeks after the CIMT treatment; only one fMRI data collection was performed on the volunteers. The functional magnetic resonance data was processed using the AFNI software package, and the functional scores of subjects were calculated using SPSS software. Second, studying the remodeling of residual brain tissue and functional compensation pathways can help to further clarify the recovery mechanism of motor function after stroke hemiplegia. Finally, compulsory exercise therapy can effectively improve upper limb motor dysfunction in stroke patients. The forced use of upper extremities during treatment induces the reorganization and compensation of cerebral cortical functional areas. This change in brain functional areas is consistent with the increase of upper extremity movement and improvement of motor function, fMRI can provide neuronal reorganization after exercise therapy evidence with compensation.

Neocortical substrates of feelings evoked with music in the ACC, insula, and somatosensory cortex

Neurobiological models of emotion focus traditionally on limbic/paralimbic regions as neural substrates of emotion generation, and insular cortex (in conjunction with isocortical anterior cingulate cortex, ACC) as the neural substrate of feelings. An emerging view, however, highlights the importance of isocortical regions beyond insula and ACC for the subjective feeling of emotions. We used music to evoke feelings of joy and fear, and multivariate pattern analysis (MVPA) to decode representations of feeling states in functional magnetic resonance (fMRI) data of n = 24 participants. Most of the brain regions providing information about feeling representations were neocortical regions. These included, in addition to granular insula and cingulate cortex, primary and secondary somatosensory cortex, premotor cortex, frontal operculum, and auditory cortex. The multivoxel activity patterns corresponding to feeling representations emerged within a few seconds, gained in strength with increasing stimulus duration, and replicated results of a hypothesis-generating decoding analysis from an independent experiment. Our results indicate that several neocortical regions (including insula, cingulate, somatosensory and premotor cortices) are important for the generation and modulation of feeling states. We propose that secondary somatosensory cortex, which covers the parietal operculum and encroaches on the posterior insula, is of particular importance for the encoding of emotion percepts, i.e., preverbal representations of subjective feeling.

Changes in Degree Centrality of Network Nodes in Different Frequency Bands in Parkinson's Disease With Depression and Without Depression.

BackgroundDepression induces an early onset of Parkinson’s disease (PD), aggravates dyskinesia and cognitive impairment, and accelerates disease progression. However, it is very difficult to identify and diagnose PD with depression (PDD) in the early clinical stage. Few studies have suggested that the changes in neural networks are associated with PDD, while degree centrality (DC) has been documented to be effective in detecting brain network changes.ObjectivesThe objectives of this study are to explore DC changes between patients with PDD and without depression (PDND) and to find the key brain hubs involved with depression in PD patients.MethodsOne hundred and four PD patients and 54 healthy controls (HCs) underwent brain resting-state functional magnetic resonance imaging. The Data Processing and Analysis of Brain Imaging and Resting-State Functional Magnetic Resonance Data Analysis Toolkit were used for processing and statistical analysis. The DC value of each frequency band was calculated. One-way analysis of variance and a two-sample t-test for post hoc comparison were used to compare the differences of the DC values in different frequency bands among PDD, PDND, and healthy control group. Gaussian random field was used for multiple comparison correction. Pearson correlation analysis was performed between each individual’s DC map and clinical indicators.ResultsThe DC value of different brain regions changed in PDD and PDND in different frequency bands. The prefrontal lobe, limbic system, and basal ganglia were the main brain regions involved. PDD patients showed a wider range and more abnormal brain areas in the slow-4 frequency band (0.027–0.073 Hz) compared to the HCs. PDD showed a decreased DC value in the medial frontal gyrus, bilateral cuneus gyrus, right lingual gyrus, bilateral supplementary motor area (SMA), bilateral superior frontal gyrus, and left paracentral lobule, but an increased DC value in the bilateral brainstem, midbrain, bilateral parahippocampal gyrus, cerebellum, left superior temporal gyrus, bilateral insula, left fusiform gyrus, and left caudate nucleus in the traditional frequency band (0.01–0.08 Hz) compared to PDND patients. PDND patients displayed more abnormal functions in the basal ganglia in the slow-4 frequency band.ConclusionThe DC changes in PDD and PDND are frequency dependent and frequency specific. The medial frontal gyrus, SMA, and limbic system may be the key hubs for depression in PD.

Pain sensitivity and the primary sensorimotor cortices: a multimodal neuroimaging study.

The primary somatosensory cortex (SI) is a critical part of the neural substrate underlying interindividual differences in pain sensitivity. Here, we investigated whether resting-state functional connectivity, gray matter density (GMD), and GABA and Glx (glutamate and glutamine) levels of the sensorimotor cortices were related to pain thresholds and whether such imaging measures could predict high and low pain sensitivity. Functional, structural, and spectroscopic magnetic resonance data were obtained from 48 healthy participants together with pain thresholds of the right index finger. Left and right sensorimotor networks (SMN) were extracted by means of independent component analysis, and GMD was measured within the combined SMN by means of voxel-based morphometry. Spectroscopic data were acquired from the bilateral sensorimotor cortices. Within the left SMN, functional connectivity to the right SI correlated positively with pain thresholds. In addition, GMD in the left SI and the GABA laterality index correlated positively with pain thresholds. A positive correlation was also found between the GABA laterality index and the left SMN connectivity to the right SI. Finally, the above mentioned functional connectivity and GMD measures could correctly predict high and low pain sensitivity in 83.7% of the study population. In summary, we showed that interindividual differences in pain sensitivity were related to the resting-state functional connectivity, interhemispheric GABA tone, and GMD of the sensorimotor cortices. Furthermore, high and low pain sensitivity could be predicted with high accuracy using imaging measures from the primary sensorimotor cortices.

Hippocampus plays a role in speech feedback processing

There is increasing evidence that the hippocampus is involved in language production and verbal communication, although little is known about its possible role. According to one view, hippocampus contributes semantic memory to spoken language. Alternatively, hippocampus is involved in the processing the (mis)match between expected sensory consequences of speaking and the perceived speech feedback. In the current study, we re-analysed functional magnetic resonance (fMRI) data of two overt picture-naming studies to test whether hippocampus is involved in speech production and, if so, whether the results can distinguish between a “pure memory” versus a “prediction” account of hippocampal involvement. In both studies, participants overtly named pictures during scanning while hearing their own speech feedback unimpededly or impaired by a superimposed noise mask. Results showed decreased hippocampal activity when speech feedback was impaired, compared to when feedback was unimpeded. Further, we found increased functional coupling between auditory cortex and hippocampus during unimpeded speech feedback, compared to impaired feedback. Finally, we found significant functional coupling between a hippocampal/supplementary motor area (SMA) interaction term and auditory cortex, anterior cingulate cortex and cerebellum during overt picture naming, but not during listening to one's own pre-recorded voice. These findings indicate that hippocampus plays a role in speech production that is in accordance with a “prediction” view of hippocampal functioning.