Unraveling somatotopic organization in the human brain using machine learning and adaptive supervoxel-based parcellations

BackWards - Unveiling the brain's topographic organization of paraspinal sensory input

Cortical reorganization and its potential pathological significance are being increasingly studied in musculoskeletal disorders such as chronic low back pain (CLBP) patients. However, detailed sensory-topographic maps of the human back are lacking, and a baseline characterization of such representations, reflecting the somatosensory organization of the healthy back, is needed before exploring potential sensory map reorganization. To this end, a novel pneumatic vibrotactile stimulation method was used to stimulate paraspinal sensory afferents, while studying their cortical representations in unprecedented detail. In 41 young healthy participants, vibrotactile stimulations at 20 Hz and 80 Hz were applied bilaterally at nine locations along the thoracolumbar axis while functional magnetic resonance imaging (fMRI) was performed. Model-based whole-brain searchlight representational similarity analysis (RSA) was used to investigate the organizational structure of brain activity patterns evoked by thoracolumbar sensory inputs. A model based on segmental distances best explained the similarity structure of brain activity patterns that were located in different areas of sensorimotor cortices, including the primary somatosensory and motor cortices and parts of the superior parietal cortex, suggesting that these brain areas process sensory input from the back in a "dermatomal" manner. The current findings provide a sound basis for testing the "cortical map reorganization theory" and its pathological relevance in CLBP.

Fully Convolutional Neural Network Deep Learning Model Fully in Patients with Type 2 Diabetes Complicated with Peripheral Neuropathy by High-Frequency Ultrasound Image

This study was aimed at exploring the diagnostic value of high-frequency ultrasound imaging based on a fully convolutional neural network (FCN) for peripheral neuropathy in patients with type 2 diabetes (T2D). A total of 70 patients with T2D mellitus were selected and divided into a lesion group (n = 31) and a nonlesion group (n = 39) according to the type of peripheral neuropathy. In addition, 30 healthy people were used as controls. Hypervoxel-based and FCN-based high-frequency ultrasound images were used to examine the three groups of patients to evaluate their diagnostic performance and to compare the changes of peripheral nerves and ultrasound characteristics. The results showed that the Dice coefficient (92.7) and mean intersection over union (mIOU) (82.6) of the proposed algorithm after image segmentation were the largest, and the Hausdorff distance (7.6) and absolute volume difference (AVD) (8.9) were the smallest. The high-frequency ultrasound based on the segmentation algorithm showed higher diagnostic accuracy (94.0% vs. 86.0%), sensitivity (87.1% vs. 67.7%), specificity (97.1% vs. 94.2%), positive predictive value (93.1% vs. 86.7%), and negative predictive value (94.4% vs. 84.0%) (P < 0.05). There were significant differences in the detection values of the three major nerve segments of the upper limbs in the control group, the lesion group, and the nonlesion group (P < 0.05). Compared with the nonlesion group, the patients in the lesion group were more likely to have reduced nerve bundle echo, blurred reticular structure, thickened epineurium, and unclear borders of adjacent tissues (P < 0.05). In summary, the high-frequency ultrasound processed by the algorithm proposed in this study showed a high diagnostic value for peripheral neuropathy in T2D patients, and high-frequency ultrasound can be used to evaluate the morphological changes of peripheral nerves in T2D patients.