Background: Metabolic-bariatric surgery (MBS) transcends weight loss and offers wide-ranging health benefits, including positive effects on brain function. However, the mechanisms behind these effects remain unclear, particularly in the context of significant postoperative changes in the inflammatory profile characteristic of MBS. Understanding how inflammation influences postoperative brain function can enhance our decision-making on patient eligibility for MBS and create new opportunities to improve the outcomes of this popular treatment.

Objective: To identify brain regions where spontaneous neural activity and functional connectivity are linked with the evolving inflammatory profile following MBS.

Methods: We investigated the relationship between the perioperative ratio of interleukin (IL)-6 to IL-10 and both the amplitude of low-frequency fluctuation (ALFF) and functional connectivity across 375 brain regions. We examined 36 patients at three time points: 1 week before, and 3 and 12 months after laparoscopic sleeve gastrectomy.

Results: Initially, the IL-6/IL-10 ratio increased during the early postoperative period but then decreased to levels lower than the preoperative values 1 year after surgery. We observed that ALFF in four subcortical structures decreased with a rising IL-6/IL-10 ratio and increased with a declining ratio. Conversely, 16 cortical regions displayed the opposite trend. Additionally, functional connectivity between the left insula and bilateral medial prefrontal cortex increased with a rising IL-6/IL-10 ratio and decreased with a declining ratio.

Conclusions: Our study is the first to identify brain regions significantly linked to inflammation after MBS. Importantly, many of the discovered areas were previously shown to be involved in the pathogenesis of obesity or are targets of contemporary medical treatments. Consequently, our findings offer valuable insights for future obesity research, especially in the context of potential therapeutic opportunities.

Keywords: bariatric surgery; clinical physiology; obesity therapy; observational study

Mucopolysaccharidosis type I (MPS I) is an inherited lysosomal storage disorder leading to deleterious brain effects. While animal models suggested that MPS I severely affects white matter (WM), whole-brain diffusion tensor imaging (DTI) analysis was not performed due to MPS-related morphological abnormalities. 3T DTI data from 28 severe (MPS IH, treated with hematopoietic stem cell transplantation-HSCT), 16 attenuated MPS I patients (MPS IA) enrolled under the study protocol NCT01870375, and 27 healthy controls (HC) were analyzed using the free-water correction (FWC) method to resolve macrostructural partial volume effects and unravel differences in DTI metrics accounting for microstructural abnormalities. FWC analysis in MPS IH compared to HC revealed higher free-water fraction (FWF) in all WM regions with increased radial (RD) and mean diffusivity (MD). Higher RD, MD, and FWF in cingulate and FWF in temporal WM were observed in MPS IA relative to HC. FWF and RD in the corpus callosum (CC) were higher in MPS IH than in MPS IA. Reaction time was correlated with fractional anisotropy (FA) in frontal and parietal WM in MPS IH. FA in temporal and central WM correlated with d-prime in MPS IA. The HSCT age was related to FA in parietal WM and FWF in frontal WM in MPS IH. FWC delineated subtype-specific WM microstructural abnormalities linked to myelination that were more extensive in MPS IH than IA, with CC findings being a key differentiator between subtypes. Earlier age at HSCT was related to preserved WM microstructure in the brain of MPS IH patients. Free water-corrected DTI distinguishes severe and attenuated MPS I patients and reveals a relationship between attention, age at HSCT, and white matter microstructure.

Background: Childhood trauma (CT) is related to altered fractional anisotropy (FA) in individuals with schizophrenia (SZ). However, it remains unclear whether CT may influence specific cellular or extracellular compartments of FA in SZ with CT experience. We extended our previous study on FA in SZ (Costello et al., 2023) and examined the impact of CT on hypothesized lower free water-corrected FA (FA_T) and higher extracellular free water (FW).

Method: Thirty-seven SZ and 129 healthy controls (HC) were grouped into the ‘none/low’ or ‘high’ CT group. All participants underwent diffusion-weighted magnetic resonance imaging. We performed tract-based spatial statistics to study the main effects of diagnostic group and CT, and the interaction between CT and diagnostic group across FA_T and FW.

Results: SZ displayed lower FA_T within the corpus callosum and corona radiata compared to HC (p < 0.05, Threshold-Free Cluster Enhancement (TFCE)). Independent of diagnosis, we observed lower FA_T (p < 0.05, TFCE) and higher FW (p < 0.05, TFCE) in both SZ and HC with high CT levels compared to SZ and HC with none or low CT levels. Furthermore, we did not identify an interaction between CT and diagnostic group (p > 0.05, TFCE).

Conclusions: These novel findings suggest that the impact of CT on lower FA_T may reflect cellular rather than extracellular alterations in established schizophrenia. This highlights the impact of CT on white matter microstructure, regardless of diagnostic status.

We propose in this paper a texture-invariant 2D keypoints descriptor specifically designed for matching preoperative Magnetic Resonance (MR) images with intraoperative Ultrasound (US) images. We introduce a matching-by-synthesis strategy, where intraoperative US images are synthesized from MR images accounting for multiple MR modalities and intraoperative US variability. We build our training set by enforcing keypoints localization over all images then train a patient-specific descriptor network that learns texture-invariant discriminant features in a supervised contrastive manner, leading to robust keypoints descriptors. Our experiments on real cases with ground truth show the effectiveness of the proposed approach, outperforming the state-of-the-art methods and achieving 80.35% matching precision on average.

Background: Current multimodal neuroimaging plays a critical role in studying clinical conditions such as cardiovascular disease, major depression, and other disorders related to chronic stress. These conditions involve the brainstem-hypothalamic network, specifically the locus coeruleus (LC), dorsal vagal complex (DVC), and paraventricular nucleus (PVN) of the hypothalamus, collectively referred to as the “DVC-LC-PVN circuitry.” This circuitry is strongly associated with the norepinephrine (NE) and epinephrine (E) neurotransmitter systems, which are implicated in the regulation of key autonomic functions, such as cardiovascular and respiratory control, stress response, and cognitive and emotional behaviors.

Keywords: Diffusion magnetic resonance imaging; Dorsal vagal complex; Locus coeruleus; Major depressive disorder; Paraventricular nucleus of the hypothalamus.

Former American football players are at risk for developing traumatic encephalopathy syndrome (TES), the clinical disorder associated with neuropathologically diagnosed chronic traumatic encephalopathy (CTE). The objective of this study was to determine whether hyposmia is present in traumatic encephalopathy syndrome. The study included 119 former professional American football players, 60 former college football players, and 58 same-age asymptomatic unexposed men from the DIAGNOSE CTE Research Project. All subjects included in the analysis had completed the Brief Smell Identification Test (B-SIT). Traumatic encephalopathy syndrome and the level of CTE certainty were diagnosed using the 2021 NINDS consensus diagnostic criteria. TES is categorized antemortem by provisional levels of increasing CTE certainty: Suggestive, Possible, and Probable. Former players who had traumatic encephalopathy syndrome and Probable CTE had lower B-SIT scores than those with TES and Suggestive CTE. Hyposmia was more likely in the former players with TES who were either CTE Possible or Probable than in those who did not have TES or had TES but were less likely to have CTE, or CTE Suggestive. There was no difference in B-SIT scores between all former players versus unexposed men nor overall between the football players with and without TES. We conclude that lower B-SIT scores may be a clinical biomarker for underlying CTE in former American football players.

Purpose: The impact of pediatric traumatic brain injury (pTBI) on health-related quality of life (HRQoL) in children and adolescents remains understudied. Short scales have some advantages in terms of economy and administration over longer scales, especially in younger children. The aim of the present study is to psychometrically evaluate the six-item German version of the QOLIBRI-OS-KID/ADO scale for children and adolescents. In addition, reference values from a general German pediatric population are obtained to assist clinicians and researchers in the interpretation of HRQoL after pTBI.

Methods: A total of 297 individuals after TBI and 1997 from a general population sample completed the questionnaire. Reliability, validity, and comparability of the assessed construct were examined.

Results: The questionnaire showed satisfactory reliability (α = 0.75 and ω = 0.81 and α = 0.85 and ω = 0.86 for the TBI and general population samples, respectively). The QOLIBRI-OS-KID/ADO was highly correlated with its long version (R² = 67%) and showed an overlap with disease-specific HRQoL (R² = 55%) in the TBI sample. The one-dimensional factorial structure could be replicated and tested for measurement invariance between samples, indicating a comparable HRQoL construct assessment. Therefore, reference values and cut-offs indicating clinically relevant impairment could be provided using percentiles stratified by factors significantly associated with the total score in the regression analyses (i.e., age group and gender).

Conclusion: In combination with the cut-offs, the QOLIBRI-OS-KID/ADO provides a cost-effective screening tool, complemented by interpretation guidelines, which may help to draw clinical conclusions and indications such as further administration of a longer version of the instrument to gain more detailed insight into impaired HRQoL domains or omission of further steps in the absence of an indication.

The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time-consuming, has high clinical and expert labor costs, and is affected by inter-observer variability. In this paper, we present a novel deep learning framework, DeepRGVP, to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure-informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a new streamline-level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non-RGVP streamlines. In the experiments, we perform comparisons with several state-of-the-art deep learning methods that were designed for tractography parcellation. Furthermore, to assess the generalizability of the proposed RGVP method, we apply our method to dMRI tractography data from neurosurgical patients with pituitary tumors. In comparison with the state-of-the-art methods, we show superior RGVP identification results using DeepRGVP with significantly higher accuracy and F1 scores. In the patient data experiment, we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP.