Morphological changes of the limbic system associated with acute and chronic low-back pain: A UK biobank imaging study.

BACKGROUND: Low back pain (LBP) is a major public health issue that influences physical and emotional factors integral to the limbic system. This study aims to investigate the association between LBP and brain morphometry alterations as the duration of LBP increases (acute vs. chronic). METHODS: We used the UK Biobank data to investigate the morphological features of the limbic system in acute LBP (N = 115), chronic LBP (N = 243) and controls (N = 358), and tried to replicate our findings with an independent dataset composed of 45 acute LBP participants evaluated at different timepoints throughout 1 year from the OpenPain database. RESULTS: We found that in comparison with chronic LBP and pain-free controls, acute LBP was associated with increased volumes of the nucleus accumbens, amygdala, hippocampus, and thalamus, and increased grey matter volumes in the hippocampus and posterior cingulate gyrus. In the replication cohort, we found non-significantly larger hippocampus and thalamus volumes in the 3-month visit (acute LBP) compared to the 1-year visit (chronic LBP), with similar effect sizes as the UK Biobank dataset. CONCLUSIONS: Our results suggest that acute LBP is associated with dramatic morphometric increases in the limbic system and mesolimbic pathway, which may reflect an active brain response and self-regulation in the early stage of LBP. SIGNIFICANCE: Our study suggests that LBP in the acute phase is associated with the brain morphometric changes (increase) in some limbic areas, indicating that the acute phase of LBP may represent a crucial stage of self-regulation and active response to the disease's onset.

Associations among acute and chronic musculoskeletal pain, sleep duration, and C-reactive protein (CRP): A cross-sectional study of the UK biobank dataset.

Both musculoskeletal pain and sleep disturbances are major health problems worldwide. Literature suggests that the two are reciprocally related and both may be associated with changes in C-reactive protein (CRP) levels. However, the relationships among musculoskeletal pain, sleep duration, and CRP remain unclear. In this cross-sectional study, we investigated the relationship between acute and chronic musculoskeletal pain, sleep, and inflammation using the data from the initial visit of the UK Biobank. 17,642 individuals with chronic musculoskeletal pain, 11,962 individuals with acute musculoskeletal pain, and 29,604 pain-free controls were included in the analysis. In addition, we validated the findings using data from the second visit assessment of the UK Biobank. We found that 1) chronic pain was associated with higher CRP levels compared to both acute pain and the pain-free controls; 2) chronic pain was associated with a lower sleep score (a measurement of sleep patterns), compared to acute pain and the pain-free controls; and acute pain was associated with lower sleep scores compared to the controls; 3) there was a significant negative association between the sleep score and CRP; 4) CRP may partially mediate the association between chronic pain and decreased sleep score. However, the effect size of the mediation was rather small, and the pathophysiological significance remains uncertain. Further validation is needed. These findings were partly replicated in the UK Biobank second visit assessment cohort with a smaller sample size. Our findings, which are based on the large UK Biobank dataset, support the interplay between musculoskeletal pain, sleep patterns, and the potential mediating role of CRP on this reciprocal relationship.

Matched neurofeedback during fMRI differentially activates reward-related circuits in active and sham groups.

BACKGROUND AND PURPOSE: Functional MRI neurofeedback (fMRI-nf) leverages the brain's ability to self-regulate its own activity. However, self-regulation processes engaged during fMRI-nf are incompletely understood. Here, we used matched feedback in an fMRI-nf experimental protocol to investigate whether brain processes recognize true neurofeedback signals. METHODS: We implemented an existing fMRI-nf protocol to train lateralized motor activity using a finger-tap task in conjunction with real-time feedback. Twelve healthy, right-handed, adult participants were assigned into age- and sex-matched active and sham study groups. Matched participant pairs received the same visual feedback, based on brain activity of the participant from the active group. We compared group-averaged activation maps before, during, and after neurofeedback, and analyzed changes in lateralized motor activity due to neurofeedback. RESULTS: Active and sham groups demonstrated different brain activation to the same feedback during neurofeedback. In particular, there was higher activation in visual cortex, secondary somatosensory cortex, and right inferior frontal gyrus in the active group compared to the sham group. Conversely, sham participants demonstrated higher activation in anterior cingulate cortex, left frontal pole, and posterior superior temporal gyrus. Despite differing brain activations during neurofeedback, neither group demonstrated significant improvement in lateralized motor activity from pre to postfeedback scan in the same session. We also observed no significant difference between pre and postfeedback activation maps, suggesting that no significant finger-tap related functional reorganization had occurred. CONCLUSIONS: These findings suggest that fMRI neurofeedback paradigms that monitor or incorporate activity from regions reported here would provide enhanced efficacy for research investigation and clinical intervention.

Perturbing fMRI brain dynamics using transcranial direct current stimulation.

The dynamic nature of resting-state functional magnetic resonance imaging (fMRI) brain activity and connectivity has drawn great interest in the past decade. Specific temporal properties of fMRI brain dynamics, including metrics such as occurrence rate and transitions, have been associated with cognition and behaviors, indicating the existence of mechanism distruption in neuropsychiatric disorders. The development of new methods to manipulate fMRI brain dynamics will advance our understanding of these pathophysiological mechanisms from native observation to experimental mechanistic manipulation. In the present study, we applied repeated transcranial direct current stimulation (tDCS) to the right dorsolateral prefrontal cortex (rDLPFC) and the left orbitofrontal cortex (lOFC), during multiple simultaneous tDCS-fMRI sessions from 81 healthy participants to assess the modulatory effects of stimulating target brain regions on fMRI brain dynamics. Using the rDLPFC and the lOFC as seeds, respectively, we first identified two reoccurring co-activation patterns (CAPs) and calculated their temporal properties (e.g., occurrence rate and transitions) before administering tDCS. The spatial maps of CAPs were associated with different cognitive and disease domains using meta-analytical decoding analysis. We then investigated how active tDCS compared to sham tDCS in the modulation of the occurrence rates of these different CAPs and perturbations of transitions between CAPs. We found that by enhancing neuronal excitability of the rDLPFC and the lOFC, the occurrence rate of one CAP was significantly decreased while that of another CAP was significantly increased during the first 6 min of stimulation. Furthermore, these tDCS-associated changes persisted over subsequent testing sessions (both during and before/after tDCS) across three consecutive days. Active tDCS could perturb transitions between CAPs and a non-CAP state (when the rDLPFC and the lOFC were not activated), but not the transitions within CAPs. These results demonstrate the feasibility of modulating fMRI brain dynamics, and open new possibilities for discovering stimulation targets and dynamic connectivity patterns that can ensure the propagation of tDCS-induced neuronal excitability, which may facilitate the development of new treatments for disorders with altered dynamics.

Computationally optimized ECoG stimulation with local safety constraints.

Direct stimulation of the cortical surface is used clinically for cortical mapping and modulation of local activity. Future applications of cortical modulation and brain-computer interfaces may also use cortical stimulation methods. One common method to deliver current is through electrocorticography (ECoG) stimulation in which a dense array of electrodes are placed subdurally or epidurally to stimulate the cortex. However, proximity to cortical tissue limits the amount of current that can be delivered safely. It may be desirable to deliver higher current to a specific local region of interest (ROI) while limiting current to other local areas more stringently than is guaranteed by global safety limits. Two commonly used global safety constraints bound the total injected current and individual electrode currents. However, these two sets of constraints may not be sufficient to prevent high current density locally (hot-spots). In this work, we propose an efficient approach that prevents current density hot-spots in the entire brain while optimizing ECoG stimulus patterns for targeted stimulation. Specifically, we maximize the current along a particular desired directional field in the ROI while respecting three safety constraints: one on the total injected current, one on individual electrode currents, and the third on the local current density magnitude in the brain. This third set of constraints creates a computational barrier due to the huge number of constraints needed to bound the current density at every point in the entire brain. We overcome this barrier by adopting an efficient two-step approach. In the first step, the proposed method identifies the safe brain region, which cannot contain any hot-spots solely based on the global bounds on total injected current and individual electrode currents. In the second step, the proposed algorithm iteratively adjusts the stimulus pattern to arrive at a solution that exhibits no hot-spots in the remaining brain. We report on simulations on a realistic finite element (FE) head model with five anatomical ROIs and two desired directional fields. We also report on the effect of ROI depth and desired directional field on the focality of the stimulation. Finally, we provide an analysis of optimization runtime as a function of different safety and modeling parameters. Our results suggest that optimized stimulus patterns tend to differ from those used in clinical practice.

Optimization of focality and direction in dense electrode array transcranial direct current stimulation (tDCS).

OBJECTIVE: Transcranial direct current stimulation (tDCS) aims to alter brain function non-invasively via electrodes placed on the scalp. Conventional tDCS uses two relatively large patch electrodes to deliver electrical current to the brain region of interest (ROI). Recent studies have shown that using dense arrays containing up to 512 smaller electrodes may increase the precision of targeting ROIs. However, this creates a need for methods to determine effective and safe stimulus patterns as the number of degrees of freedom is much higher with such arrays. Several approaches to this problem have appeared in the literature. In this paper, we describe a new method for calculating optimal electrode stimulus patterns for targeted and directional modulation in dense array tDCS which differs in some important aspects with methods reported to date. APPROACH: We optimize stimulus pattern of dense arrays with fixed electrode placement to maximize the current density in a particular direction in the ROI. We impose a flexible set of safety constraints on the current power in the brain, individual electrode currents, and total injected current, to protect subject safety. The proposed optimization problem is convex and thus efficiently solved using existing optimization software to find unique and globally optimal electrode stimulus patterns. MAIN RESULTS: Solutions for four anatomical ROIs based on a realistic head model are shown as exemplary results. To illustrate the differences between our approach and previously introduced methods, we compare our method with two of the other leading methods in the literature. We also report on extensive simulations that show the effect of the values chosen for each proposed safety constraint bound on the optimized stimulus patterns. SIGNIFICANCE: The proposed optimization approach employs volume based ROIs, easily adapts to different sets of safety constraints, and takes negligible time to compute. An in-depth comparison study gives insight into the relationship between different objective criteria and optimized stimulus patterns. In addition, the analysis of the interaction between optimized stimulus patterns and safety constraint bounds suggests that more precise current localization in the ROI, with improved safety criterion, may be achieved by careful selection of the constraint bounds.

Optimizing Stimulus Patterns for Dense Array tDCS With Fewer Sources Than Electrodes Using A branch and Bound Algorithm.

Dense array transcranial direct current stimulation (tDCS) has become of increasing interest as a noninvasive modality to modulate brain function. To target a particular brain region of interest (ROI), using a dense electrode array placed on the scalp, the current injection pattern can be appropriately optimized. Previous optimization methods have assumed availability of individually controlled current sources for each non-reference electrode. This may be costly and impractical in a clinical setting. However, using fewer current sources than electrodes results in a non-convex combinatorial optimization problem. In this paper, we present a novel use of the branch and bound (BB) algorithm to find sub-optimal stimulus patterns with fewer current sources than electrodes. We present simulation results for both focal and spatially extended cortical ROIs. Our results suggest that only a few (2-3) independently controlled current sources can achieve comparable results to a full set (125 sources) to a tolerance of 5%. BB is computationally 3-5 orders of magnitude less demanding than exhaustive search.