Face recognition ability can be predicted by microstructural properties of white matter: a study of diffusion tensor imaging (DTI).

There is a great individual difference in people's face recognition ability (FRA). This study aimed to reveal the neural mechanism underlying such individual differences. Elastic-net regression models were constructed to predict FRA based on the white matter (WM) microstructural properties. We found that FRA can be accurately predicted by the WM microstructural properties. For the right inferior longitudinal fasciculus (ILF) and bilateral arcuate fasciculus (AF), FRA was correlated negatively to fractional anisotropy (FA), but positively to radial diffusivity (RD). In contrast, for the corpus callosum forceps minor (CFM), FRA was correlated positively to FA, but negatively to RD. Such various patterns of the WM microstructural properties suggested a positive correlation between FRA and fiber diameter for the right ILF and bilateral AF, but a negative correlation between FRA and diameter of the CFM. These findings reflected that FRA was correlated positively to connectivities of the right ILF and bilateral AF, but negatively to those of the CFM. These findings not only confirmed the significant role of the right ILF in face recognition, but also revealed the involvement of the bilateral AF and CFM in face recognition, particularly implying the important role of hemisphere lateralization modulated by transcallosal connectivity in face recognition.

The Detection of Invisible Abnormal Metabolism in the FDG-PET Images of Patients With Anti-LGI1 Encephalitis by Machine Learning.

Objective: This study aims to detect the invisible metabolic abnormality in PET images of patients with anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis using a multivariate cross-classification method. Methods: Participants were divided into two groups, namely, the training cohort and the testing cohort. The training cohort included 17 healthy participants and 17 patients with anti-LGI1 encephalitis whose metabolic abnormality was able to be visibly detected in both the medial temporal lobe and the basal ganglia in their PET images [completely detectable (CD) patients]. The testing cohort included another 16 healthy participants and 16 patients with anti-LGI1 encephalitis whose metabolic abnormality was not able to be visibly detected in the medial temporal lobe and the basal ganglia in their PET images [non-completely detectable (non-CD) patients]. Independent component analysis (ICA) was used to extract features and reduce dimensions. A logistic regression model was constructed to identify the non-CD patients. Results: For the testing cohort, the accuracy of classification was 90.63% with 13 out of 16 non-CD patients identified and all healthy participants distinguished from non-CD patients. The patterns of PET signal changes resulting from metabolic abnormalities related to anti-LGI1 encephalitis were similar for CD patients and non-CD patients. Conclusion: This study demonstrated that multivariate cross-classification combined with ICA could improve, to some degree, the detection of invisible abnormal metabolism in the PET images of patients with anti-LGI1 encephalitis. More importantly, the invisible metabolic abnormality in the PET images of non-CD patients showed patterns that were similar to those seen in CD patients.

A two-center radiomic analysis for differentiating major depressive disorder using multi-modality MRI data under different parcellation methods.

BACKGROUND: The present study aimed to explore the difference in the brain function and structure between patients with major depressive disorder (MDD) and healthy controls (HCs) using two-center and multi-modal MRI data, which would be helpful to investigate the pathogenesis of MDD. METHODS: The subjects were collected from two hospitals. One including 140 patients with MDD and 138 HCs was used as primary cohort. Another one including 29 patients with MDD and 52 HCs was used as validation cohort. Functional and structural magnetic resonance images (MRI) were acquired to extract four types of features: functional connectivity (FC), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and gray matter volume (GMV). Then classifiers using different combinations among the four types of selected features were respectively built to discriminate patients from HCs. Different templates were applied and the results under different templates were compared. RESULTS: The classifier built with the combination of FC, ALFF, and GMV under the AAL template discriminated patients from HCs with the best performance (AUC=0.916, ACC=84.8%). The regions selected in all the different templates were mainly located in the default mode network, affective network, prefrontal cortex. LIMITATIONS: First, the sample size of the validation cohort was limited. Second, diffusion tensor imaging data were not collected. CONCLUSION: The performance of classifier was improved by using multi-modal MRI imaging. Different templates would be suitable for different types of analysis. The regions selected in all the different templates are possibly the core regions to investigate the pathophysiology of MDD.

The Impacts of Obstructive Sleep Apnea Severity on Brain White Matter Integrity and Cognitive Functions in Children: A Diffusion Tensor Imaging Study.

OBJECTIVE: To investigate the impacts of obstructive sleep apnea (OSA) on white matter (WM) integrity and cognitive functions of pediatric patients with different levels of OSA severity. METHODS: Fifty-eight children with OSA and thirty-four healthy controls (HC) were recruited. All participants underwent diffusion tensor imaging (DTI) examination, polysomnography (PSG), and neurocognitive assessments. Patients were divided into mild OSA (MG) and moderate-severe OSA (SG) groups. WM integrity, PSG data, and neurocognitive assessment scores were compared among those groups. RESULTS: For apnea hypopnea index (AHI), obstructive apnea hypopnea index (OAHI), arousal index, SpO2 nadir, and attention, SG was worse than both MG and HC with MG worse than HC. For baseline SpO2 and intelligence, SG was worse than both MG and HC with no significant difference between MG and HC. Impaired WM integrity was observed in bilateral anterior thalamic radiation, bilateral inferior fronto-occipital fasciculus, bilateral inferior longitudinal fasciculus, right superior longitudinal fasciculus, right hippocampus, left cingulate gyrus, right uncinate fasciculus, callosum forceps major, and callosum forceps minor only for SG than for HC. WM integrity was significantly correlated with OSA severity and neurocognitive assessment scores only for SG, but not for MG. CONCLUSION: Decreased baseline SpO2, WM impairment, and intelligence decline were all observed only for SG, but not for MG, implying an associated relationship among decreased SpO2, WM impairment and WM impairment. Thus, for SG, additional assessments of brain damage and cognitive function decline are needed for prognostic evaluation of OSA.

Effects of obstructive sleep apnoea severity on neurocognitive and brain white matter alterations in children according to sex: a tract-based spatial statistics study.

OBJECTIVES: To investigate alterations in neurocognitive, attention, paediatric sleep questionnaire (PSQ) scores and whole brain white matter (WM) integrity between children with mild and severe obstructive sleep apnoea (OSA) according to sex and whether these changes are associated with OSA severity. METHODS: Fifty-seven children (36 males and 21 females) diagnosed with OSA were recruited for this study. Children of both sexes were divided into mild (male-MG, female-MG) and severe (male-SG, female-SG) groups according to OSA severity. Polysomnography (PSG), neurocognitive, attention and PSQ tests were compared between groups by one-way samples analysis of variance (ANOVA) F test. Diffusion tensor imaging (DTI) was scanned using a 3T GE MRI scanner and analysed by Tract-based Spatial Statistics (TBSS). Spearman correlation was calculated between DTI Eigenvalues and clinical characteristics. RESULTS: Compared to mild OSA patients, severe OSA patients presented greater severity of obstructive apnoea hypopnea index (OAHI), neurocognition, PSQ and attention tests in both male and female patients. Brain WM integrity in the male-SG, compared to the male-MG, demonstrated significantly reduced fractional anisotropy (FA) values in the right middle frontal gyrus and the right frontal sub-gyral regions and increased axial diffusivity (AD) values in the right inferior frontal gyrus, left parietal angular gyrus and sub-gyral regions, while no differences were found between the female-MG and female-SG. Alterations in male-SG brain regions were observably correlated with severity in male OSA patients. CONCLUSIONS: The integrity of WM, which regulates autonomic, cognitive, and attention functions, is impaired in male, but not female, children with severe OSA.

Semi-quantitative FDG-PET Analysis Increases the Sensitivity Compared With Visual Analysis in the Diagnosis of Autoimmune Encephalitis.

Objective: The purpose of this study is to evaluate the potential diagnostic benefit of SPM-based semi-quantitative FDG-PET analysis in autoimmune encephalitis (AE) compared with visual analysis by experienced neuroradiologists using a larger sample size. Methods: This observational retrospective case series study was conducted from a tertiary epilepsy center between May 2014 and March 2017. Healthy individuals without any neurologic or psychiatric diseases were recruited as control. We determined brain FDG-PET abnormal glucose metabolism on medial temporal lobe and basal ganglia using semi-quantitative analysis and compared this method with visual analysis at the same time among patients with autoantibody positive AE. Results: Twenty-eight patients with clinically diagnosed AE and 53 healthy individuals without any neurologic or psychiatric diseases were recruited. On the medial temporal lobe and the basal ganglia, semi-quantitative analysis showed consistency with the visual assessment for whom they had abnormal metabolism by visual assessment. More importantly, 56% patients on medial temporal lobe and 73% patients on the basal ganglia respectively who were not identified by visual inspection can be detected by semi-quantitative analysis, demonstrating the greater sensitivity of semi-quantitative analysis compared with visual assessment. Significance: This study showed semi-quantitative brain FDG-PET analysis was better than visual analysis in view of observing the abnormal glucose metabolism of patients with autoantibody positive AE. Semi-quantitative FDG-PET analysis appears to be a helpful tool in early diagnosis of patients with AE.

The Fusiform Face Area Plays a Greater Role in Holistic Processing for Own-Race Faces Than Other-Race Faces.

Own-race faces are recognized more effectively than other-race faces. This phenomenon is referred to as other-race effect (ORE). Existing behavioral evidence suggests that one of the possible causes of ORE is that own-race faces are processed more holistically than other-race faces. However, little is known about whether such differences in processing also produce distinctive neural responses in the cortical face processing network. To bridge this gap, the present study used fMRI methodology and the composite face paradigm to examine the response patterns of the traditional face-preferential cortical areas (i.e., the bilateral fusiform face areas [FFA] and the bilateral occipital face areas [OFA]) elicited by own-race faces and other-race faces. We found that the right FFA exhibited a neural composite face effect only for own-race faces but not for other-race faces, even with the absence of the race-related difference in behavior composite face effect. These findings suggest that the right FFA plays a greater role in holistic processing of individual own-race faces than other-race faces. They also suggest that the neural composite effect observed in the right FFA is not the exact neural counterpart of the behavioral face composite effect. The findings of the present study revealed that, along the pathway of the bottom-up face processing, own-race faces and other-race faces presented the holistic processing difference as early as when they were processed in the right FFA.

Development of Effective Connectivity during Own- and Other-Race Face Processing: A Granger Causality Analysis.

Numerous developmental studies have suggested that other-race effect (ORE) in face recognition emerges as early as in infancy and develops steadily throughout childhood. However, there is very limited research on the neural mechanisms underlying this developmental ORE. The present study used Granger causality analysis (GCA) to examine the development of children's cortical networks in processing own- and other-race faces. Children were between 3 and 13 years. An old-new paradigm was used to assess their own- and other-race face recognition with ETG-4000 (Hitachi Medical Co., Japan) acquiring functional near infrared spectroscopy (fNIRS) data. After preprocessing, for each participant and under each face condition, we obtained the causal map by calculating the weights of causal relations between the time courses of [oxy-Hb] of each pair of channels using GCA. To investigate further the differential causal connectivity for own-race faces and other-race faces at the group level, a repeated measure analysis of variance (ANOVA) was performed on the GCA weights for each pair of channels with the face race task (own-race face vs. other-race face) as the within-subject variable and the age as a between-subject factor (continuous variable). We found an age-related increase in functional connectivity, paralleling a similar age-related improvement in behavioral face processing ability. More importantly, we found that the significant differences in neural functional connectivity between the recognition of own-race faces and that of other-race faces were modulated by age. Thus, like the behavioral ORE, the neural ORE emerges early and undergoes a protracted developmental course.

Linking Resting-State Networks in the Prefrontal Cortex to Executive Function: A Functional Near Infrared Spectroscopy Study.

Executive function (EF) plays vital roles in our everyday adaptation to the ever-changing environment. However, limited existing studies have linked EF to the resting-state brain activity. The functional connectivity in the resting state between the sub-regions of the brain can reveal the intrinsic neural mechanisms involved in cognitive processing of EF without disturbance from external stimuli. The present study investigated the relations between the behavioral executive function (EF) scores and the resting-state functional network topological properties in the Prefrontal Cortex (PFC). We constructed complex brain functional networks in the PFC from 90 healthy young adults using functional near infrared spectroscopy (fNIRS). We calculated the correlations between the typical network topological properties (regional topological properties and global topological properties) and the scores of both the Total EF and components of EF measured by computer-based Cambridge Neuropsychological Test Automated Battery (CANTAB). We found that the Total EF scores were positively correlated with regional properties in the right dorsal superior frontal gyrus (SFG), whereas the opposite pattern was found in the right triangular inferior frontal gyrus (IFG). Different EF components were related to different regional properties in various PFC areas, such as planning in the right middle frontal gyrus (MFG), working memory mainly in the right MFG and triangular IFG, short-term memory in the left dorsal SFG, and task switch in the right MFG. In contrast, there were no significant findings for global topological properties. Our findings suggested that the PFC plays an important role in individuals' behavioral performance in the executive function tasks. Further, the resting-state functional network can reveal the intrinsic neural mechanisms involved in behavioral EF abilities.