Multimodal fusion of multiple rest fMRI networks and MRI gray matter via parallel multilink joint ICA reveals highly significant function/structure coupling in Alzheimer's disease.

In this article, we focus on estimating the joint relationship between structural magnetic resonance imaging (sMRI) gray matter (GM), and multiple functional MRI (fMRI) intrinsic connectivity networks (ICNs). To achieve this, we propose a multilink joint independent component analysis (ml-jICA) method using the same core algorithm as jICA. To relax the jICA assumption, we propose another extension called parallel multilink jICA (pml-jICA) that allows for a more balanced weight distribution over ml-jICA/jICA. We assume a shared mixing matrix for both the sMRI and fMRI modalities, while allowing for different mixing matrices linking the sMRI data to the different ICNs. We introduce the model and then apply this approach to study the differences in resting fMRI and sMRI data from patients with Alzheimer's disease (AD) versus controls. The results of the pml-jICA yield significant differences with large effect sizes that include regions in overlapping portions of default mode network, and also hippocampus and thalamus. Importantly, we identify two joint components with partially overlapping regions which show opposite effects for AD versus controls, but were able to be separated due to being linked to distinct functional and structural patterns. This highlights the unique strength of our approach and multimodal fusion approaches generally in revealing potentially biomarkers of brain disorders that would likely be missed by a unimodal approach. These results represent the first work linking multiple fMRI ICNs to GM components within a multimodal data fusion model and challenges the typical view that brain structure is more sensitive to AD than fMRI.

Robust hierarchically organized whole-brain patterns of dysconnectivity in schizophrenia spectrum disorders observed after personalized intrinsic network topography.

BACKGROUND: Spatial patterns of brain functional connectivity can vary substantially at the individual level. Applying cortical surface-based approaches with individualized rather than group templates may accelerate the discovery of biological markers related to psychiatric disorders. We investigated cortico-subcortical networks from multi-cohort data in people with schizophrenia spectrum disorders (SSDs) and healthy controls (HC) using individualized connectivity profiles. METHODS: We utilized resting-state and anatomical MRI data from n = 406 participants (n = 203 SSD, n = 203 HC) from four cohorts. Functional timeseries were extracted from previously defined intrinsic network subregions of the striatum, thalamus, and cerebellum as well as 80 cortical regions of interest, representing six intrinsic networks using (1) volume-based approaches, (2) a surface-based group atlas approaches, and (3) Personalized Intrinsic Network Topography (PINT). RESULTS: The correlations between all cortical networks and the expected subregions of the striatum, cerebellum, and thalamus were increased using a surface-based approach (Cohen's D volume vs. surface 0.27-1.00, all p < 10-6 ) and further increased after PINT (Cohen's D surface vs. PINT 0.18-0.96, all p < 10-4 ). In SSD versus HC comparisons, we observed robust patterns of dysconnectivity that were strengthened using a surface-based approach and PINT (Number of differing pairwise-correlations: volume: 404, surface: 570, PINT: 628, FDR corrected). CONCLUSION: Surface-based and individualized approaches can more sensitively delineate cortical network dysconnectivity differences in people with SSDs. These robust patterns of dysconnectivity were visibly organized in accordance with the cortical hierarchy, as predicted by computational models.

Neural Signatures of Pain Modulation in Short-Term and Long-Term Mindfulness Training: A Randomized Active-Control Trial.

OBJECTIVE: Mindfulness-based interventions are widely used to target pain, yet their neural mechanisms of action are insufficiently understood. The authors studied neural and subjective pain response in a randomized active-control trial of mindfulness-based stress reduction (MBSR) alongside long-term meditation practitioners. METHODS: Healthy participants (N=115) underwent functional neuroimaging during a thermal acute pain task before and after random assignment to MBSR (N=28), an active control condition (health enhancement program [HEP]) (N=32), or a waiting list control condition (N=31). Long-term meditators (N=30) completed the same neuroimaging paradigm. Pain response was measured via self-reported intensity and unpleasantness, and neurally via two multivoxel machine-learning-derived signatures: the neurologic pain signature (NPS), emphasizing nociceptive pain processing, and the stimulus intensity independent pain signature-1 (SIIPS1), emphasizing stimulus-independent neuromodulatory processes. RESULTS: The MBSR group showed a significant decrease in NPS response relative to the HEP group (Cohen's d=-0.43) and from pre- to postintervention assessment (d=-0.47). The MBSR group showed small, marginal decreases in NPS relative to the waiting list group (d=-0.36), and in SIIPS1 relative to both groups (HEP group, d=-0.37; waiting list group, d=-0.37). In subjective unpleasantness, the MBSR and HEP groups also showed modest significant reductions compared with the waiting list group (d=-0.45 and d=-0.55). Long-term meditators reported significantly lower pain than nonmeditators but did not differ in neural response. Within the long-term meditator group, cumulative practice during intensive retreat was significantly associated with reduced SIIPS1 (r=-0.65), whereas daily practice was not. CONCLUSIONS: Mindfulness training showed associations with pain reduction that implicate differing neural pathways depending on extent and context of practice. Use of neural pain signatures in randomized trials offers promise for guiding the application of mindfulness interventions to pain treatment.

Functional Magnetic Resonance Imaging Study of Electroacupuncture Stimulating Uterine Acupoints.

OBJECTIVE: Based on resting-state functional magnetic resonance imaging (rs-fMRI), to observe the changes of brain function of bilateral uterine points stimulated by electroacupuncture, so as to provide imaging basis for acupuncture in the treatment of gynecological and reproductive diseases. METHODS: 20 healthy female subjects were selected to stimulate bilateral uterine points (EX-CA1) by electroacupuncture. FMRI data before and after acupuncture were collected. The ReHo values before and after acupuncture were compared by using the analysis method of regional homogeneity (ReHo) of the whole brain, so as to explore the regulatory effect of acupuncture intervention on brain functional activities of healthy subjects. RESULTS: Compared with before acupuncture, the ReHo values of the left precuneus lobe, left central posterior gyrus, calcarine, left lingual gyrus, and cerebellum decreased significantly after acupuncture. CONCLUSION: Electroacupuncture at bilateral uterine points can induce functional activities in brain areas such as the precuneus, cerebellum, posterior central gyrus, talform sulcus, and lingual gyrus. The neural activities in these brain areas may be related to reproductive hormone level, emotional changes, somatic sensation, and visual information. It can clarify the neural mechanism of acupuncture at uterine points in the treatment of reproductive and gynecological diseases to a certain extent.

Association of executive function capacity with gait motor imagery ability and PFC activity: An fNIRS study.

Individual differences exist in gait motor imagery ability. However, little is known about the underlying neural mechanisms. We previously conducted a study using functional near-infrared spectroscopy (fNIRS), which showed that participants who overestimated mental walking times to a greater degree exhibited greater activation in the right prefrontal cortex (PFC). The PFC is implicated in executive functions (EFs), including working memory (WM). Thus, this study investigated whether individual differences in EF capacity are associated with gait motor imagery ability and PFC activity. Thirty volunteers participated (mean age: 21.7 ± 1.8 years) in the study. Their EF capacity was assessed by the Trail Making Test - Part B (TMT-B). We measured the accuracy of gait motor imagery and PFC activity during mental walking using fNIRS, while changing task difficulty by varying the path width. The results showed that the overestimation of mental walking time over actual walking time and right PFC activity increased with an increase in the TMT-B times. These results suggest that the EF capacity, including WM, is strongly associated with gait motor imagery ability and right PFC activity. The brain network that includes the right PFC may play an important role in the maintenance and manipulation of gait motor imagery.

Inter-individual differences in pain anticipation and pain perception in migraine: Neural correlates of migraine frequency and cortisol-to-dehydroepiandrosterone sulfate (DHEA-S) ratio.

Previous studies targeting inter-individual differences in pain processing in migraine mainly focused on the perception of pain. Our main aim was to disentangle pain anticipation and perception using a classical fear conditioning task, and investigate how migraine frequency and pre-scan cortisol-to-dehydroepiandrosterone sulfate (DHEA-S) ratio as an index of neurobiological stress response would relate to neural activation in these two phases. Functional Magnetic Resonance Imaging (fMRI) data of 23 participants (18 females; mean age: 27.61± 5.36) with episodic migraine without aura were analysed. We found that migraine frequency was significantly associated with pain anticipation in brain regions comprising the midcingulate and caudate, whereas pre-scan cortisol-to DHEA-S ratio was related to pain perception in the pre-supplementary motor area (pre-SMA). Both results suggest exaggerated preparatory responses to pain or more general to stressors, which may contribute to the allostatic load caused by stressors and migraine attacks on the brain.

Neural substrates of human fear generalization: A 7T-fMRI investigation.

Fear generalization - the tendency to interpret ambiguous stimuli as threatening due to perceptual similarity to a learned threat - is an adaptive process. Overgeneralization, however, is maladaptive and has been implicated in a number of anxiety disorders. Neuroimaging research has indicated several regions sensitive to effects of generalization, including regions involved in fear excitation (e.g., amygdala, insula) and inhibition (e.g., ventromedial prefrontal cortex). Research has suggested several other small brain regions may play an important role in this process (e.g., hippocampal subfields, bed nucleus of the stria terminalis [BNST], habenula), but, to date, these regions have not been examined during fear generalization due to limited spatial resolution of standard human neuroimaging. To this end, we utilized the high spatial resolution of 7T fMRI to characterize the neural circuits involved in threat discrimination and generalization. Additionally, we examined potential modulating effects of trait anxiety and intolerance of uncertainty on neural activation during threat generalization. In a sample of 31 healthy undergraduate students, significant positive generalization effects (i.e., greater activation for stimuli with increasing perceptual similarity to a learned threat cue) were observed in the visual cortex, thalamus, habenula and BNST, while negative generalization effects were observed in the dentate gyrus, CA1, and CA3. Associations with individual differences were underpowered, though preliminary findings suggested greater generalization in the insula and primary somatosensory cortex may be correlated with self-reported anxiety. Overall, findings largely support previous neuroimaging work on fear generalization and provide additional insight into the contributions of several previously unexplored brain regions.

Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.

Cortical and subcortical hemodynamic changes during sleep slow waves in human light sleep.

EEG slow waves, the hallmarks of NREM sleep are thought to be crucial for the regulation of several important processes, including learning, sensory disconnection and the removal of brain metabolic wastes. Animal research indicates that slow waves may involve complex interactions within and between cortical and subcortical structures. Conventional EEG in humans, however, has a low spatial resolution and is unable to accurately describe changes in the activity of subcortical and deep cortical structures. To overcome these limitations, here we took advantage of simultaneous EEG-fMRI recordings to map cortical and subcortical hemodynamic (BOLD) fluctuations time-locked to slow waves of light sleep. Recordings were performed in twenty healthy adults during an afternoon nap. Slow waves were associated with BOLD-signal increases in the posterior brainstem and in portions of thalamus and cerebellum characterized by preferential functional connectivity with limbic and somatomotor areas, respectively. At the cortical level, significant BOLD-signal decreases were instead found in several areas, including insula and somatomotor cortex. Specifically, a slow signal increase preceded slow-wave onset and was followed by a delayed, stronger signal decrease. Similar hemodynamic changes were found to occur at different delays across most cortical brain areas, mirroring the propagation of electrophysiological slow waves, from centro-frontal to inferior temporo-occipital cortices. Finally, we found that the amplitude of electrophysiological slow waves was positively related to the magnitude and inversely related to the delay of cortical and subcortical BOLD-signal changes. These regional patterns of brain activity are consistent with theoretical accounts of the functions of sleep slow waves.

Functional neuroimaging in hearing research and audiology.

The various methods of medical imaging are essential for many diagnostic issues in clinical routine, e.g., for the diagnostics and localisation of tumorous diseases, or for the clarification of other lesions in the central nervous system. In addition to these classical roles both positron emission tomography (PET) and magnetic resonance imaging (MRI) allow for the investigation of functional processes in the human brain, when used in a specific way. The last 25 years have seen great progress, especially with respect to functional MRI, in terms of the available experimental paradigms as well as the data analysis strategies, so that a directed investigation of neurophysiological correlates of psychoacoustic performance is possible. This covers fundamental measures of sound perception like loudness and pitch, specific audiological symptoms like tinnitus, which often accompanies hearing disorders, but it also includes experiments on speech perception or on virtual acoustic environments. One important aspect common to many auditory neuroimaging studies is the central question at what stage in the human auditory pathway the sensory coding of the incoming sound is transformed into a universal and context-dependent perceptual representation, which is the basis for what we hear. This overview summarises findings from the literature as well as a few studies from our lab, to discuss the possibilities and the limits of the adoption of functional neuroimaging methods in audiology. Up to this stage, most auditory neuroimaging studies have investigated basic processes in normal hearing listeners. However, the hitherto existing results suggest that the methods of auditory functional neuroimaging - possibly complemented by electrophysiological methods like EEG and MEG - have a great potential to contribute to a deeper understanding of the processes and the impact of hearing disorders.

Neonatal Brain Response to Deviant Auditory Stimuli and Relation to Maternal Trait Anxiety.

OBJECTIVE: Excessive response to unexpected or "deviant" stimuli during infancy and early childhood represents an early risk marker for anxiety disorders. However, research has yet to delineate the specific brain regions underlying the neonatal response to deviant stimuli near birth and the relation to risk for anxiety disorders. The authors used task-based functional MRI (fMRI) to delineate the neonatal response to deviant stimuli and its relationship to maternal trait anxiety. METHODS: The authors used fMRI to measure brain activity evoked by deviant auditory stimuli in 45 sleeping neonates (mean age, 27.8 days; 60% female; 64% African American). In 41 of the infants, neural response to deviant stimuli was examined in relation to maternal trait anxiety on the State-Trait Anxiety Inventory, a familial risk factor for offspring anxiety. RESULTS: Neonates manifested a robust and widespread neural response to deviant stimuli that resembles patterns found previously in adults. Higher maternal trait anxiety was related to higher responses within multiple brain regions, including the left and right anterior insula, the ventrolateral prefrontal cortex, and multiple areas within the anterior cingulate cortex. These areas overlap with brain regions previously linked to anxiety disorders and other psychiatric illnesses in adults. CONCLUSIONS: The neural architecture sensitive to deviant stimuli robustly functions in newborns. Excessive responsiveness of some circuitry components at birth may signal risk for anxiety and other psychiatric disorders.

Cigarette craving modulation is more feasible than resistance modulation for heavy cigarette smokers: empirical evidence from functional MRI data.

BACKGROUND: Modulation of cigarette craving and neuronal activations from nicotine-dependent cigarette smokers using real-time functional MRI (rtfMRI)-based neurofeedback (rtfMRI-NF) has been previously reported. OBJECTIVES: The aim of this study was to evaluate the efficacy of rtfMRI-NF training in reducing cigarette cravings using fMRI data acquired before and after training. METHODS: Treatment-seeking male heavy cigarette smokers (N = 14) were enrolled and randomly assigned to two conditions related to rtfMRI-NF training aiming at resisting the urge to smoke. In one condition, subjects underwent conventional rtfMRI-NF training using neuronal activity as the neurofeedback signal (activity-based) within regions-of-interest (ROIs) implicated in cigarette craving. In another condition, subjects underwent rtfMRI-NF training with additional functional connectivity information included in the neurofeedback signal (functional connectivity-added). Before and after rtfMRI-NF training at each of two visits, participants underwent two fMRI runs with cigarette smoking stimuli and were asked to crave or resist the urge to smoke without neurofeedback. Cigarette craving-related or resistance-related regions were identified using a general linear model followed by paired t-tests and were evaluated using regression analysis on the basis of neuronal activation and subjective craving scores (CRSs). RESULTS: Visual areas were mainly implicated in craving, whereas the superior frontal areas were associated with resistance. The degree of (a) CRS reduction and (b) the correlation between neuronal activation and CRSs were statistically significant (P < 0.05) in the functional connectivity-added neurofeedback group for craving-related ROIs. CONCLUSION: Our study demonstrated the feasibility of altering cigarette craving in craving-related ROIs but not in resistance-related ROIs via rtfMRI-NF training.

Study for cerebral central network mechanism of acupuncture stimulation quantity based on changes of cerebral functional connection of fMRI.

BACKGROUND: Ischemic stroke is a major chronic noninfectious disease that seriously endangers health. Acupuncture is effective for ischemic stroke and less adverse reactions. However, there is not enough clinical trial data and solid evidence could confirm how acupuncture work to cerebral functional connectivity changes, and whether the changes is related to the different stimulation quantity. DESIGN: This is a multicenter, central-randomized, controlled, double-blind, noninferiority, 2 factors and 3 levels orthogonal clinical trial. A total of 100 participants with ischemic stroke aged from 40 to 80 were randomized into experimental group and control group, the experimental group was divided into 9 groups (A1-A9) according to different factors or levels, and each group have 10 participants. The whole study period is 17 days, including 1 week for baseline observation, 3 days treatment and observation, and 1 week follow-up. Primary outcome is the fMRI based on blood oxygenation level dependent. Secondary outcomes included National Institute of Health Stroke Scale, Modified Barthel Index, Brunnstrom stroke recovery, stroke Chinese medicine symptom. Clinical assessments will be evaluated at before and the 0 hour, 24 hours, 36 hours after treatment, and 1 week follow-up. The primary outcome of the postacupuncture effect were investigated by paired T-test, and the continuous outcome variables will be analyzed with univariate repetitive measurement deviation analysis. Adverse events will be noted and recorded for the safety evaluation. CONCLUSION: The purpose of this study was to evaluate the central mechanism of acupuncture stimulation quantity using time and frequency as control conditions. This study will provide reasonable stimulation parameters and strong mechanism evidence of cerebral central network for the use of acupuncture for ischemic stroke. CHICTR REGISTRATION NUMBER: ChiCTR1900023169. Registered 15 May 2019.

Repetitive transcranial magnetic stimulation as a potential treatment approach for cannabis use disorder.

The expanding legalization of cannabis across the United States is associated with increases in cannabis use, and accordingly, an increase in the number and severity of individuals with cannabis use disorder (CUD). The lack of FDA-approved pharmacotherapies and modest efficacy of psychotherapeutic interventions means that many of those who seek treatment for CUD relapse within the first few months. Consequently, there is a pressing need for innovative, evidence-based treatment development for CUD. Preliminary evidence suggests that repetitive transcranial magnetic stimulation (rTMS) may be a novel, non-invasive therapeutic neuromodulation tool for the treatment of a variety of substance use disorders (SUDs), including recently receiving FDA clearance (August 2020) for use as a smoking cessation aid in tobacco cigarette smokers. However, the potential of rTMS for CUD has not yet been reviewed. This paper provides a primer on therapeutic neuromodulation techniques for SUDs, with a particular focus on reviewing the current status of rTMS research in people who use cannabis. Lastly, future directions are proposed for rTMS treatment development in CUD, with suggestions for study design parameters and clinical endpoints based on current gold-standard practices for therapeutic neuromodulation research.

Early protein intake predicts functional connectivity and neurocognition in preterm born children.

Nutritional intake can promote early neonatal brain development in very preterm born neonates (< 32 weeks' gestation). In a group of 7-year-old very preterm born children followed since birth, we examined whether early nutrient intake in the first weeks of life would be associated with long-term brain function and neurocognitive skills at school age. Children underwent resting-state functional MRI (fMRI), intelligence testing (Wechsler Intelligence Scale for Children, 5th Ed) and visual-motor processing (Beery-Buktenica, 5th Ed) at 7 years. Relationships were assessed between neonatal macronutrient intakes, functional connectivity strength between thalamic and default mode networks (DMN), and neuro-cognitive function using multivariable regression. Greater functional connectivity strength between thalamic networks and DMN was associated with greater intake of protein in the first week (ß = 0.17; 95% CI 0.11, 0.23, p < 0.001) but lower intakes of fat (ß = - 0.06; 95% CI - 0.09, - 0.02, p = 0.001) and carbohydrates (ß = - 0.03; 95% CI - 0.04, - 0.01, p = 0.003). Connectivity strength was also associated with protein intake during the first month (ß = 0.22; 95% CI 0.06, 0.37, p = 0.006). Importantly, greater thalamic-DMN connectivity strength was associated with higher processing speed indices (ß = 26.9; 95% CI 4.21, 49.49, p = 0.02) and visual processing scores (ß = 9.03; 95% CI 2.27, 15.79, p = 0.009). Optimizing early protein intake may contribute to promoting long-term brain health in preterm-born children.

Putamen volume predicts real-time fMRI neurofeedback learning success across paradigms and neurofeedback target regions.

Real-time fMRI guided neurofeedback training has gained increasing interest as a noninvasive brain regulation technique with the potential to modulate functional brain alterations in therapeutic contexts. Individual variations in learning success and treatment response have been observed, yet the neural substrates underlying the learning of self-regulation remain unclear. Against this background, we explored potential brain structural predictors for learning success with pooled data from three real-time fMRI data sets. Our analysis revealed that gray matter volume of the right putamen could predict neurofeedback learning success across the three data sets (n = 66 in total). Importantly, the original studies employed different neurofeedback paradigms during which different brain regions were trained pointing to a general association with learning success independent of specific aspects of the experimental design. Given the role of the putamen in associative learning this finding may reflect an important role of instrumental learning processes and brain structural variations in associated brain regions for successful acquisition of fMRI neurofeedback-guided self-regulation.

Subject-independent decoding of affective states using functional near-infrared spectroscopy.

Affective decoding is the inference of human emotional states using brain signal measurements. This approach is crucial to develop new therapeutic approaches for psychiatric rehabilitation, such as affective neurofeedback protocols. To reduce the training duration and optimize the clinical outputs, an ideal clinical neurofeedback could be trained using data from an independent group of volunteers before being used by new patients. Here, we investigated if this subject-independent design of affective decoding can be achieved using functional near-infrared spectroscopy (fNIRS) signals from frontal and occipital areas. For this purpose, a linear discriminant analysis classifier was first trained in a dataset (49 participants, 24.65±3.23 years) and then tested in a completely independent one (20 participants, 24.00±3.92 years). Significant balanced accuracies between classes were found for positive vs. negative (64.50 ± 12.03%, p<0.01) and negative vs. neutral (68.25 ± 12.97%, p<0.01) affective states discrimination during a reactive block consisting in viewing affective-loaded images. For an active block, in which volunteers were instructed to recollect personal affective experiences, significant accuracy was found for positive vs. neutral affect classification (71.25 ± 18.02%, p<0.01). In this last case, only three fNIRS channels were enough to discriminate between neutral and positive affective states. Although more research is needed, for example focusing on better combinations of features and classifiers, our results highlight fNIRS as a possible technique for subject-independent affective decoding, reaching significant classification accuracies of emotional states using only a few but biologically relevant features.

Greater Somatosensory Afference With Acupuncture Increases Primary Somatosensory Connectivity and Alleviates Fibromyalgia Pain via Insular γ-Aminobutyric Acid: A Randomized Neuroimaging Trial.

OBJECTIVE: Acupuncture is a complex multicomponent treatment that has shown promise in the treatment of fibromyalgia (FM). However, clinical trials have shown mixed results, possibly due to heterogeneous methodology and lack of understanding of the underlying mechanism of action. The present study was undertaken to understand the specific contribution of somatosensory afference to improvements in clinical pain, and the specific brain circuits involved. METHODS: Seventy-six patients with FM were randomized to receive either electroacupuncture (EA), with somatosensory afference, or mock laser acupuncture (ML), with no somatosensory afference, twice a week over 8 treatments. Patients with FM in each treatment group were assessed for pain severity levels, measured using Brief Pain Inventory (BPI) scores, and for levels of functional brain network connectivity, assessed using resting state functional magnetic resonance imaging (MRI) and proton magnetic resonance spectroscopy in the right anterior insula, before and after treatment. RESULTS: Fibromyalgia patients who received EA therapy experienced a greater reduction in pain severity, as measured by the BPI, compared to patients who received ML therapy (mean difference in BPI from pre- to posttreatment was -1.14 in the EA group versus -0.46 in the ML group; P for group × time interaction = 0.036). Participants receiving EA treatment, as compared to ML treatment, also exhibited resting functional connectivity between the primary somatosensory cortical representation of the leg (S1leg ; i.e. primary somatosensory subregion activated by EA) and the anterior insula. Increased S1leg -anterior insula connectivity was associated with both reduced levels of pain severity as measured by the BPI (r = -0.44, P = 0.01) and increased levels of γ-aminobutyric acid (GABA+) in the anterior insula (r = 0.48, P = 0.046) following EA therapy. Moreover, increased levels of GABA+ in the anterior insula were associated with reduced levels of pain severity as measured by the BPI (r = -0.59, P = 0.01). Finally, post-EA treatment changes in levels of GABA+ in the anterior insula mediated the relationship between changes in S1leg -anterior insula connectivity and pain severity on the BPI (bootstrap confidence interval -0.533, -0.037). CONCLUSION: The somatosensory component of acupuncture modulates primary somatosensory functional connectivity associated with insular neurochemistry to reduce pain severity in FM.

Neural endophenotypes and predictors of laryngeal dystonia penetrance and manifestation.

Focal dystonias are the most common forms of isolated dystonia; however, the etiopathophysiological signatures of disorder penetrance and clinical manifestation remain unclear. Using an imaging genetics approach, we investigated functional and structural representations of neural endophenotypes underlying the penetrance and manifestation of laryngeal dystonia in families, including 21 probands and 21 unaffected relatives, compared to 32 unrelated healthy controls. We further used a supervised machine-learning algorithm to predict the risk for dystonia development in susceptible individuals based on neural features of identified endophenotypes. We found that abnormalities in prefrontal-parietal cortex, thalamus, and caudate nucleus were commonly shared between patients and their unaffected relatives, representing an intermediate endophenotype of laryngeal dystonia. Machine learning classified 95.2% of unaffected relatives as patients rather than healthy controls, substantiating that these neural alterations represent the endophenotypic marker of dystonia penetrance, independent of its symptomatology. Additional abnormalities in premotor-parietal-temporal cortical regions, caudate nucleus, and cerebellum were present only in patients but not their unaffected relatives, likely representing a secondary endophenotype of dystonia manifestation. Based on alterations in the parietal cortex and caudate nucleus, the machine learning categorized 28.6% of unaffected relative as patients, indicating their increased lifetime risk for developing clinical manifestation of dystonia. The identified endophenotypic neural markers may be implemented for screening of at-risk individuals for dystonia development, selection of families for genetic studies of novel variants based on their risk for disease penetrance, or stratification of patients who would respond differently to a particular treatment in clinical trials.

Near-infrared light spectroscopy and stimulation in cognitive neuroscience: the need for an integrative view?

Near-infrared spectroscopy (NIRS) has been largely used in neuroscience as an alternative non-invasive neuroimaging technique, primarily to measure the oxygenation levels of cerebral haemoglobin. Its portability and relative robustness against motion artefacts made it an ideal method to measure cerebral blood changes during physical activity. Usually referred to as 'functional' NIRS (fNIRS) when used to monitor brain changes during motor or cognitive tasks, this technique often involves the montage the probes on the forehead of the participants to gauge the neurophysiological underpinning of executive functioning. Other applications of NIRS include other aspects of cerebral hemodynamics such as cerebral pulsatility. However, there is an important aspect that fNIRS studies do not seem to have taken into account so far, which relates to the capacity of near-infrared light to modulate cognitive and psychological processes according to what is known as photobiomodulation (PBM). Hence, drawing on a selection of NIRS and PBM experiments, we argue in favour of an integrative view for NIR-based neuroimaging studies, which should embrace a control for the possible effects of light stimulation, especially when fNIRS is considered to test the effect of an intervention.