A powerful and efficient multivariate approach for voxel-level connectome-wide association studies

Decoding auditory deprivation: resting-state fMRI insights into deafness and brain plasticity

Deafness, as a profound manifestation of sensory deprivation, prompts a cascade of intricate cerebral adaptations. In this study, involving 35 deaf individuals and 35 hearing controls, we utilized resting-state functional magnetic resonance imaging (rs-fMRI) to delve into the depths of functional connectivity nuances distinguishing deaf individuals from their hearing counterparts. Leading our analytical approach was the application of multi-voxel pattern analysis (fc-MVPA). This advanced method provided a refined perspective, revealing amplified neural connectivity within the deaf population. Notably, regions such as the left postcentral somatosensory association cortex, the anterior and posterior corridors of the left superior temporal gyrus (STG), and the left mid-temporal lobe were identified as hotspots of heightened connectivity. Further, fc-MVPA shed light on intricate interaction effects, which became more pronounced when examining variables such as the duration of auditory deprivation and the extent of sign language exposure. These interactions were particularly evident in the premotor and left frontal mid-orbital regions. Complementing this, seed-based connectivity assessments illuminated pronounced coupling dynamics within the left STG spectrum. Concurrently, local correlation (LCOR) value analysis in the deaf group revealed significant shifts in the right superior STG and bilateral precuneus. In addition, amplitude of low-frequency fluctuation (ALFF) evaluations indicated modulations in the bilateral mid cingulum and left superior mid frontal gyrus. This comprehensive, fc-MVPA-driven exploration uncovers the multifaceted functional adaptations resulting from deafness, highlighting the profound plasticity of the human brain and its potential implications for targeted rehabilitative strategies.

Orbitofrontal cortex connectivity is associated with food reward and body weight in humans

The aim was to investigate with very large-scale analyses whether there are underlying functional connectivity differences between humans that relate to food reward and whether these in turn are associated with being overweight. In 37 286 humans from the UK Biobank, resting-state functional connectivities of the orbitofrontal cortex (OFC), especially with the anterior cingulate cortex, were positively correlated with the liking for sweet foods (False Discovery Rate (FDR) P < 0.05). They were also positively correlated with the body mass index (BMI) (FDR P < 0.05). Moreover, in a sample of 502 492 people, the 'liking for sweet foods' was correlated with their BMI (r = 0.06, P < 10-125). In a cross-validation with 545 participants from the Human Connectome Project, a higher functional connectivity involving the OFC relative to other brain areas was associated with a high BMI (≥30) compared to a mid-BMI group (22-25; P = 6 × 10-5), and low OFC functional connectivity was associated with a low BMI (≤20.5; P < 0.024). It is proposed that a high BMI relates to increased efficacy of OFC food reward systems and a low BMI to decreased efficacy. This was found with no stimulation by food, so may be an underlying individual difference in brain connectivity that is related to food reward and BMI.

A new natural detector for irradiations with blue LED light source in photodynamic therapy measurements via UV-Vis spectroscopy

Photodynamic therapy has been recently studied, bringing innovations regarding the reduction of exposure time to light by the patient. This work aimed to investigate the feasibility of using Coutarea hexandra (Jacq.) K. Schum (CHS) as a detector in photodynamic therapy measurements. For this, an irradiator containing a blue LED bulb lamp was utilized. The CHS samples were irradiated with ten doses from 0.60 up to 6.0 kJ/cm², and six concentrations were prepared (1, 2, 3, 4, 5, and 6 mg/ml) for the CHS detector samples. After irradiation, the detector samples were evaluated using UV-Vis spectrophotometry. The results showed the behavior of the CHS detector with doses and concentrations, its sensitivity, and its linearity was also evaluated both by Wavelength Method (WM) and the Kernel Principal Component Regression (KPCR) Statistical Method. The values obtained indicate that this method can be applied to the CHS sample detector. In conclusion, the CHS is a promising detector in the field of photodynamic therapy.

Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals

Individual differences in working memory relate to performance differences in general cognitive ability. The neural bases of such individual differences, however, remain poorly understood. Here, using a data-driven technique known as connectome-based predictive modeling, we built models to predict individual working memory performance from whole-brain functional connectivity patterns. Using n-back or rest data from the Human Connectome Project, connectome-based predictive models significantly predicted novel individuals' 2-back accuracy. Model predictions also correlated with measures of fluid intelligence and, with less strength, sustained attention. Separate fluid intelligence models predicted working memory score, as did sustained attention models, again with less strength. Anatomical feature analysis revealed significant overlap between working memory and fluid intelligence models, particularly in utilization of prefrontal and parietal regions, and less overlap in predictive features between working memory and sustained attention models. Furthermore, showing the generality of these models, the working memory model developed from Human Connectome Project data generalized to predict memory in an independent data set of 157 older adults (mean age = 69 years; 48 healthy, 54 amnestic mild cognitive impairment, 55 Alzheimer disease). The present results demonstrate that distributed functional connectivity patterns predict individual variation in working memory capability across the adult life span, correlating with constructs including fluid intelligence and sustained attention.

Global signal regression strengthens association between resting-state functional connectivity and behavior

Global signal regression (GSR) is one of the most debated preprocessing strategies for resting-state functional MRI. GSR effectively removes global artifacts driven by motion and respiration, but also discards globally distributed neural information and introduces negative correlations between certain brain regions. The vast majority of previous studies have focused on the effectiveness of GSR in removing imaging artifacts, as well as its potential biases. Given the growing interest in functional connectivity fingerprinting, here we considered the utilitarian question of whether GSR strengthens or weakens associations between resting-state functional connectivity (RSFC) and multiple behavioral measures across cognition, personality and emotion. By applying the variance component model to the Brain Genomics Superstruct Project (GSP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 47% across 23 behavioral measures after GSR. In the Human Connectome Project (HCP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 40% across 58 behavioral measures, when GSR was applied after ICA-FIX de-noising. To ensure generalizability, we repeated our analyses using kernel regression. GSR improved behavioral prediction accuracies by an average of 64% and 12% in the GSP and HCP datasets respectively. Importantly, the results were consistent across methods. A behavioral measure with greater RSFC-explained variance (using the variance component model) also exhibited greater prediction accuracy (using kernel regression). A behavioral measure with greater improvement in behavioral variance explained after GSR (using the variance component model) also enjoyed greater improvement in prediction accuracy after GSR (using kernel regression). Furthermore, GSR appeared to benefit task performance measures more than self-reported measures. Since GSR was more effective at removing motion-related and respiratory-related artifacts, GSR-related increases in variance explained and prediction accuracies were unlikely the result of motion-related or respiratory-related artifacts. However, it is worth emphasizing that the current study focused on whole-brain RSFC, so it remains unclear whether GSR improves RSFC-behavioral associations for specific connections or networks. Overall, our results suggest that at least in the case for young healthy adults, GSR strengthens the associations between RSFC and most (although not all) behavioral measures. Code for the variance component model and ridge regression can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/preprocessing/Li2019_GSR.