Structural connectivity changes in unilateral hearing loss.

Network connectivity, as mapped by the whole brain connectome, plays a crucial role in regulating auditory function. Auditory deprivation such as unilateral hearing loss might alter structural network connectivity; however, these potential alterations are poorly understood. Thirty-seven acoustic neuroma patients with unilateral hearing loss (19 left-sided and 18 right-sided) and 19 healthy controls underwent diffusion-weighted and T1-weighted imaging to assess edge strength, node strength, and global efficiency of the structural connectome. Edge strength was estimated by pair-wise normalized streamline density from tractography and connectomics. Node strength and global efficiency were calculated through graph theory analysis of the connectome. Pure-tone audiometry and word recognition scores were used to correlate the degree and duration of unilateral hearing loss with node strength and global efficiency. We demonstrate significantly stronger edge strength and node strength through the visual network, weaker edge strength and node strength in the somatomotor network, and stronger global efficiency in the unilateral hearing loss patients. No discernible correlations were observed between the degree and duration of unilateral hearing loss and the measures of node strength or global efficiency. These findings contribute to our understanding of the role of structural connectivity in hearing by facilitating visual network upregulation and somatomotor network downregulation after unilateral hearing loss.

Hippocampus diffusivity abnormalities in classical trigeminal neuralgia.

Introduction: Patients with chronic pain frequently report cognitive symptoms that affect memory and attention, which are functions attributed to the hippocampus. Trigeminal neuralgia (TN) is a chronic neuropathic pain disorder characterized by paroxysmal attacks of unilateral orofacial pain. Given the stereotypical nature of TN pain and lack of negative symptoms including sensory loss, TN provides a unique model to investigate the hippocampal implications of chronic pain. Recent evidence demonstrated that TN is associated with macrostructural hippocampal abnormalities indicated by reduced subfield volumes; however, there is a paucity in our understanding of hippocampal microstructural abnormalities associated with TN. Objectives: To explore diffusivity metrics within the hippocampus, along with its functional and structural subfields, in patients with TN. Methods: To examine hippocampal microstructure, we utilized diffusion tensor imaging in 31 patients with TN and 21 controls. T1-weighted magnetic resonance images were segmented into hippocampal subfields and registered into diffusion-weighted imaging space. Fractional anisotropy (FA) and mean diffusivity were extracted for hippocampal subfields and longitudinal axis segmentations. Results: Patients with TN demonstrated reduced FA in bilateral whole hippocampi and hippocampal body and contralateral subregions CA2/3 and CA4, indicating microstructural hippocampal abnormalities. Notably, patients with TN showed significant correlation between age and hippocampal FA, while controls did not exhibit this correlation. These effects were driven chiefly by female patients with TN. Conclusion: This study demonstrates that TN is associated with microstructural hippocampal abnormalities, which may precede and potentially be temporally linked to volumetric hippocampal alterations demonstrated previously. These findings provide further evidence for the role of the hippocampus in chronic pain and suggest the potential for targeted interventions to mitigate cognitive symptoms in patients with chronic pain.

Brain imaging signatures of neuropathic facial pain derived by artificial intelligence.

Advances in neuroimaging have permitted the non-invasive examination of the human brain in pain. However, a persisting challenge is in the objective differentiation of neuropathic facial pain subtypes, as diagnosis is based on patients' symptom descriptions. We use artificial intelligence (AI) models with neuroimaging data to distinguish subtypes of neuropathic facial pain and differentiate them from healthy controls. We conducted a retrospective analysis of diffusion tensor and T1-weighted imaging data using random forest and logistic regression AI models on 371 adults with trigeminal pain (265 classical trigeminal neuralgia (CTN), 106 trigeminal neuropathic pain (TNP)) and 108 healthy controls (HC). These models distinguished CTN from HC with up to 95% accuracy, and TNP from HC with up to 91% accuracy. Both classifiers identified gray and white matter-based predictive metrics (gray matter thickness, surface area, and volume; white matter diffusivity metrics) that significantly differed across groups. Classification of TNP and CTN did not show significant accuracy (51%) but highlighted two structures that differed between pain groups-the insula and orbitofrontal cortex. Our work demonstrates that AI models with brain imaging data alone can differentiate neuropathic facial pain subtypes from healthy data and identify regional structural indicates of pain.