NeuroImage
-
A substantial body of evidence documents massive reorganization of primary sensory and motor cortices following hand amputation, the extent of which is correlated with phantom limb pain. Many therapies for phantom limb pain are based upon the idea that plastic changes after amputation are maladaptive and attempt to normalize representations of cortical areas adjacent to the hand area. Recent data suggest, however, that higher levels of phantom pain are associated with stronger local activity and more structural integrity in the missing hand area rather than with reorganization of neighbouring body parts. ⋯ We observed different reorganizational patterns for all three body parts as the former hand area showed few signs of reorganization, but the lip and elbow representations reorganized and shifted towards the hand area. We also found that poorer voluntary control and higher levels of pain in the phantom limb were powerful drivers of the lip and elbow topological changes. In addition to providing further support for the maladaptative plasticity model, we demonstrate for the first time that motor capacities of the phantom limb correlate with post-amputation reorganization, and that this reorganization is not limited to the face and hand representations but also includes the proximal upper-limb.
-
Randomized Controlled Trial
From Pavlov to pain: How predictability affects the anticipation and processing of visceral pain in a fear conditioning paradigm.
Conditioned pain-related fear may contribute to hyperalgesia and central sensitization, but this has not been tested for interoceptive, visceral pain. The underlying ability to accurately predict pain is based on predictive cue properties and may alter the sensory processing and cognitive-emotional modulation of pain thus exacerbating the subjective pain experience. In this functional magnetic resonance imaging study using painful rectal distensions as unconditioned stimuli (US), we addressed changes in the neural processing of pain during the acquisition of pain-related fear and subsequently tested if conditioned stimuli (CS) contribute to hyperalgesia and increased neural responses in pain-encoding regions. ⋯ With regard to activation in response to painful stimuli, the unpredictable compared to the predictable group revealed greater activation in pain-encoding (somatosensory cortex, insula) and pain-modulatory (prefrontal and cingulate cortices, periaqueductal grey, parahippocampus) regions. In the test phase, no evidence of hyperalgesia or central sensitization was found, but the predictable group demonstrated enhanced caudate nucleus activation in response to CS(-)-signaled pain. These findings support that during fear conditioning, the ability to predict pain affects neural processing of visceral pain and alters the associative learning processes underlying the acquisition of predictive properties of cues signaling pain, but conditioned pain-related fear does not result in visceral hyperalgesia or central sensitization.
-
Structural magnetic resonance imaging studies have documented reduced gray matter in acutely ill patients with anorexia nervosa to be at least partially reversible following weight restoration. However, few longitudinal studies exist and the underlying mechanisms of these structural changes are elusive. In particular, the relative speed and completeness of brain structure normalization during realimentation remain unknown. ⋯ This pattern of thinning in illness and rapid normalization during weight rehabilitation was largely mirrored in subcortical volumes. Together, our findings indicate that structural brain insults inflicted by starvation in anorexia nervosa may be reversed at a rate much faster than previously thought if interventions are successful before the disorder becomes chronic. This provides evidence drawing previously speculated mechanisms such as (de-)hydration and neurogenesis into question and suggests that neuronal and/or glial remodeling including changes in macromolecular content may underlie the gray matter alterations observed in anorexia nervosa.
-
In quantitative PET/MR imaging, attenuation correction (AC) of PET data is markedly challenged by the need of deriving accurate attenuation maps from MR images. A number of strategies have been developed for MRI-guided attenuation correction with different degrees of success. In this work, we compare the quantitative performance of three generic AC methods, including standard 3-class MR segmentation-based, advanced atlas-registration-based and emission-based approaches in the context of brain time-of-flight (TOF) PET/MRI. ⋯ The standard 3-class MRAC method significantly underestimated cerebral PET tracer uptake. While current state-of-the-art MLAA-AC methods look promising, they were unable to noticeably reduce quantification errors in the context of brain imaging. Conversely, the proposed atlas-AC method provided the most accurate attenuation maps, and thus the lowest quantification bias.
-
Identifying diffuse axonal injury (DAI) in patients with traumatic brain injury (TBI) presenting with normal appearing radiological MRI presents a significant challenge. Neuroimaging methods such as diffusion MRI and probabilistic tractography, which probe the connectivity of neural networks, show significant promise. We present a machine learning approach to classify TBI participants primarily with mild traumatic brain injury (mTBI) based on altered structural connectivity patterns derived through the network based statistical analysis of structural connectomes generated from TBI and age-matched control groups. ⋯ The analysis revealed altered connectivity within a number of intra- and inter-hemispheric white matter pathways associated with DAI, in consensus with existing literature. A mean classification accuracy of 68.16%±1.81% and mean sensitivity of 80.0%±2.36% were achieved in correctly classifying the TBI patients evaluated on the subset of the participants that was not used for the statistical analysis, in a 10-fold cross-validation framework. These results highlight the potential for statistical machine learning approaches applied to structural connectomes to identify patients with diffusive axonal injury.