Bilateral Parahippocampal Gyrus Research Articles

Sex Differences in Human Brain Structure at Birth

BackgroundSex differences in human brain anatomy have been well-documented, though remain significantly underexplored during early development. The neonatal period is a critical stage for brain development and can provide key insights into the role that prenatal and early postnatal factors play in shaping sex differences in the brain.MethodsHere, we assessed on-average sex differences in global and regional brain volumes in 514 newborns aged 0–28 days (236 birth-assigned females and 278 birth-assigned males) using data from the developing Human Connectome Project. We also assessed sex-by-age interactions to investigate sex differences in early postnatal brain development.ResultsOn average, males had significantly larger intracranial and total brain volumes, even after controlling for birth weight. After controlling for total brain volume, females showed significantly greater total cortical gray matter volumes, whilst males showed greater total white matter volumes. After controlling for total brain volume in regional comparisons, females had significantly increased white matter volumes in the corpus callosum and increased gray matter volumes in the bilateral parahippocampal gyri (posterior parts), left anterior cingulate gyrus, bilateral parietal lobes, and left caudate nucleus. Males had significantly increased gray matter volumes in the right medial and inferior temporal gyrus (posterior part) and right subthalamic nucleus. Effect sizes ranged from small for regional comparisons to large for global comparisons. Significant sex-by-age interactions were noted in the left anterior cingulate gyrus and left superior temporal gyrus (posterior parts).ConclusionsOur findings demonstrate that sex differences in brain structure are already present at birth and remain comparatively stable during early postnatal development, highlighting an important role of prenatal factors in shaping sex differences in the brain.

Limbic Network Derangement Mediates Unawareness of Apathy in Mild Cognitive Impairment due to Alzheimer's Disease: Clues from [18F]FDG PET Voxel-Wise Analysis.

Discrepancy between caregiver and patient assessments of apathy in mild cognitive impairment (MCI) is considered an index of apathy unawareness, independently predicting progression to AD dementia. However, its neural underpinning are uninvestigated. To explore the [18F]FDG PET-based metabolic correlates of apathy unawareness measured through the discrepancy between caregiver and patient self-report, in patients diagnosed with MCI. We retrospectively studied 28 patients with an intermediate or high likelihood of MCI-AD, progressed to dementia over an average of two years, whose degree of apathy was evaluated by means of the Apathy Evaluation Scale (AES) for both patients (PT-AES) and caregivers (CG-AES). Voxel-based analysis at baseline was used to obtain distinct volumes of interest (VOIs) correlated with PT-AES, CG-AES, or their absolute difference (DISCR-AES). The resulting DISCR-AES VOI count densities were used as covariates in an inter-regional correlation analysis (IRCA) in MCI-AD patients and a group of matched healthy controls (HC). DISCR-AES negatively correlated with metabolism in bilateral parahippocampal gyrus, posterior cingulate cortex, and thalamus, PT-AES score with frontal and anterior cingulate areas, while there was no significant correlation between CG-AES and brain metabolism. IRCA revealed that MCI-AD patients exhibited reduced metabolic/functional correlations of the DISCR-AES VOI with the right cingulate gyrus and its anterior projections compared to HC. Apathy unawareness entails early disruption of the limbic circuitry rather than the classical frontal-subcortical pathways typically associated with apathy. This reaffirms apathy unawareness as an early and independent measure in MCI-AD, marked by distinct pathophysiological alterations.

Quantitative assessment of brain structural abnormalities in children with autism spectrum disorder based on artificial intelligence automatic brain segmentation technology and machine learning methods

Rationale and objectivesTo explore the characteristics of brain structure in Chinese children with autism spectrum disorder (ASD) using artificial intelligence automatic brain segmentation technique, and to diagnose children with ASD using machine learning (ML) methods in combination with structural magnetic resonance imaging (sMRI) features. MethodsA total of 60 ASD children and 48 age- and sex-matched typically developing (TD) children were prospectively enrolled from January 2023 to April 2024. All subjects were scanned using 3D-T1 sequences. Automated brain segmentation techniques were utilized to obtain the standardized volume of each brain structure (the ratio of the absolute volume of brain structure to the whole brain volume). The standardized volumes of each brain structure in the two groups were statistically compared, and the volume data of brain areas with significant differences were combined with ML methods to diagnose and predict ASD patients. ResultsCompared with the TD group, the volumes of the right lateral orbitofrontal cortex, right medial orbitofrontal cortex, right pars opercularis, right pars triangularis, left hippocampus, bilateral parahippocampal gyrus, left fusiform gyrus, right superior temporal gyrus, bilateral insula, bilateral inferior parietal cortex, right precuneus cortex, bilateral putamen, left pallidum, and right thalamus were significantly increased in the ASD group (P< 0.05). Among six ML algorithms, support vector machine (SVM) and adaboost (AB) had better performance in differentiating subjects with ASD from those TD children, with their average area under curve (AUC) reaching 0.91 and 0.92, respectively. ConclusionAutomatic brain segmentation technology based on artificial intelligence can rapidly and directly measure and display the volume of brain structures in children with autism spectrum disorder and typically developing children. Children with ASD show abnormalities in multiple brain structures, and when paired with sMRI features, ML algorithms perform well in the diagnosis of ASD.

Disrupted individual-level morphological brain network in spinal muscular atrophy types 2 and 3.

Spinal muscular atrophy (SMA) is one of the most common monogenic neuromuscular diseases, and the pathogenesis mechanisms, especially the brain network topological properties, remain unknown. This study aimed to use individual-level morphological brain network analysis to explore the brain neural network mechanisms in SMA. Individual-level gray matter (GM) networks were constructed by estimating the interregional similarity of GM volume distribution using both Kullback-Leibler divergence-based similarity (KLDs) and Jesen-Shannon divergence-based similarity (JSDs) measurements based on Automated Anatomical Labeling 116 and Hammersmith 83 atlases for 38 individuals with SMA types 2 and 3 and 38 age- and sex-matched healthy controls (HCs). The topological properties were analyzed by the graph theory approach and compared between groups by a nonparametric permutation test. Additionally, correlation analysis was used to assess the associations between altered topological metrics and clinical characteristics. Compared with HCs, although global network topology remained preserved in individuals with SMA, brain regions with altered nodal properties mainly involved the right olfactory gyrus, right insula, bilateral parahippocampal gyrus, right amygdala, right thalamus, left superior temporal gyrus, left cerebellar lobule IV-V, bilateral cerebellar lobule VI, right cerebellar lobule VII, and vermis VII and IX. Further correlation analysis showed that the nodal degree of the right cerebellar lobule VII was positively correlated with the disease duration, and the right amygdala was negatively correlated with the Hammersmith Functional Motor Scale Expanded (HFMSE) scores. Our findings demonstrated that topological reorganization may prioritize global properties over nodal properties, and disrupted topological properties in the cortical-limbic-cerebellum circuit in SMA may help to further understand the network pathogenesis underlying SMA.

Graph Theory Further Revealed Visual Spatial Working Memory Impairment in Patients with Inflammatory Bowel Disease.

Inflammatory Bowel Disease (IBD) patients may experience cognitive impairments in Visuospatial Working Memory (VSWM), significantly impacting their quality of life. However, the mechanisms underlying these impairments remain poorly understood. We studied functional MRI and graph theory analysis to investigate changes in functional connectivity networks during the Mental Rotation Task (MRT) in IBD patients. Twenty IBD patients (13 males, 7 females; mean age = 34.95 ± 13.80 years; mean disease duration = 2.43 ± 2.37 years) participated in the study. Exclusion criteria encompassed recent use of analgesics, 5-Aminosalicylate, corticosteroids, or immunosuppressants within the past three months. Additionally, we recruited 20 age-, gender-, and education-matched healthy controls for comparison. Compared to a control group, IBD patients exhibited significantly longer reaction times and reduced accuracy during the MRT. Our analysis revealed abnormalities in multiple nodal attributes within the functional connectivity network, particularly in regions such as the bilateral orbitofrontal cortex, right supplementary motor area, bilateral parahippocampal gyrus, and bilateral anterior temporal lobe. We observed that the nodal efficiency in the left temporal pole is negatively correlated with Red Blood Cell Distribution Width (RDW) and positively correlated with response time of MRT. Our findings revealed notable abnormalities in multiple node attributes among IBD patients during MRT, providing evidence of cognitive impairments in VSWM in IBD patients. This study found RDW maybe can serve as a clinical indicator for predicting early VSWM impairment in patients with IBD.

과학 관련 미래 상상하기 과정에서 나타나는 뇌활동성 분석

We investigated the neural bases of future imagination in science using functional magnetic resonance imaging (fMRI). Participants were 10 science and engineering undergraduate students. Seven of them (70%) received sciencegifted education when they went through primary, middle, or high school, and all participants got good grade in science and engineering class. In the future imagination task, participants were asked to imagine the science relatedfuture based on the sentence. In the control task, participants were asked to read the science related sentence by eye. Analysis showed that future imagination task produced greater brain activation than control task in bilateralparahippocampal gyrus and fusiform gyrus (BA 37), bilateral temporal cortex (BA 39), left superior frontal cortex (BA 6), right prefrontal cortex (BA 46), bilateral cerebellum, and so on. These areas are involved in long term memory, generating visual mental image, integrating sensory information, spatial working memory, self-initiated retrieval and so on. Thus, our result suggest that future imagination process in science involves complex brain network. It could provide the brain scientific explanation for the meaning of the future imagination activities in science gifted education.

Association Between Serum NT-proBNP and Gray Matter Atrophy Patterns in an Older Japanese Population: The Hisayama Study.

Several population-based studies have reported that higher serum N-terminal pro-brain natriuretic peptide (NT-proBNP) levels are associated with brain morphological changes. However, no population-based studies have examined the relationship between serum NT-proBNP and various regional brain volumes in detail. We here analyzed the brain MRI data of 1201 community-dwelling Japanese aged ≥65 years. Regional gray matter volumes (GMV) and intracranial volume (ICV) were estimated by applying voxel-based morphometry (VBM) methods. The associations of serum NT-proBNP with regional GMV/ICV were examined by analysis of covariance. The regional gray matter atrophy patterns associated with elevated serum NT-proBNP levels were investigated using VBM without a priori regions of interest. The multivariable-adjusted means of the frontal, temporal, hippocampal, parahippocampal, and entorhinal GMV/ICV decreased significantly with elevated serum NT-proBNP levels (all p for trend and q values of false discovery rate correction < .05). In VBM, elevated serum NT-proBNP levels were correlated with atrophy of the bilateral hippocampi, bilateral amygdalas, bilateral parahippocampal gyri, bilateral entorhinal areas, bilateral fusiform gyri, left middle temporal gyrus, left inferior temporal gyrus, right central operculum, right posterior orbital gyrus, bilateral middle frontal gyri, anterior cingulate gyrus and bilateral medial frontal cortices. In a sensitivity analysis excluding 254 participants with mild cognitive impairment or dementia, serum NT-proBNP levels were correlated with atrophy of the bilateral hippocampi, bilateral amygdalas, bilateral parahippocampal gyri, bilateral fusiform gyri, and left middle frontal gyrus. Our data suggest that elevated serum NT-proBNP levels are associated with gray matter atrophy in brain regions that play an important role in cognitive function.

Longitudinal brain changes in Parkinson's disease with severe olfactory deficit

IntroductionOlfactory dysfunction and REM sleep behavior disorder (RBD) are associated with distinct cognitive trajectories in the course of Parkinson's disease (PD). The underlying neurobiology for this relationship remains unclear but may involve distinct patterns of neurodegeneration. This study aimed to examine longitudinal cortical atrophy and thinning in early-stage PD with severe olfactory deficit (anosmia) without and with concurrent probable RBD. MethodsLongitudinal MRI data over four years of 134 de novo PD and 49 healthy controls (HC) from the Parkinson Progression Marker Initiative (PPMI) cohort were analyzed using a linear mixed-effects model. Patients were categorized into those with anosmia by the University of Pennsylvania Smell Identification Test (UPSIT) score ≤ 18 (AO+) and those without (UPSIT score > 18, AO-). The AO+ group was further subdivided into AO+ with probable RBD (AO+RBD+) and without (AO+RBD-) for subanalysis. ResultsCompared to subjects without baseline anosmia, the AO+ group exhibited greater longitudinal declines in both volume and thickness in the bilateral parahippocampal gyri and right transverse temporal gyrus. Patients with concurrent anosmia and RBD showed more extensive longitudinal declines in cortical volume and thickness, involving additional brain regions including the bilateral precuneus, left inferior temporal gyrus, right paracentral gyrus, and right precentral gyrus. ConclusionsThe atrophy/thinning patterns in early-stage PD with severe olfactory dysfunction include regions that are critical for cognitive function and could provide a structural basis for previously reported associations between severe olfactory deficit and cognitive decline in PD. Concurrent RBD might enhance the dynamics of cortical changes.

Alterations in cortical volume and complexity in Parkinson's disease with depression.

The aim of this study is to investigate differences in gray matter volume and cortical complexity between Parkinson's disease with depression (PDD) patients and Parkinson's disease without depression (PDND) patients. A total of 41 PDND patients, 36 PDD patients, and 38 healthy controls (HC) were recruited and analyzed by Voxel-based morphometry (VBM) and surface-based morphometry (SBM). Differences in gray matter volume and cortical complexity were compared using the one-way analysis of variance (ANOVA) and correlated with the Hamilton Depression Scale-17 (HAMD-17) scores. PDD patients exhibited significant cortical atrophy in various regions, including bilateral medial parietal-occipital-temporal lobes, right dorsolateral temporal lobes, bilateral parahippocampal gyrus, and bilateral hippocampus, compared to HC and PDND groups. A negative correlation between the GMV of left precuneus and HAMD-17 scores in the PDD group tended to be significant (r = -0.318, p = 0.059). Decreased gyrification index was observed in the bilateral insular and dorsolateral temporal cortex. However, there were no significant differences found in fractal dimension and sulcal depth. Our research shows extensive cortical structural changes in the insular cortex, parietal-occipital-temporal lobes, and hippocampal regions in PDD. This provides a morphological perspective for understanding the pathophysiological mechanism underlying depression in Parkinson's disease.

Gray matter covariations in autism: out-of-sample replication using the ENIGMA autism cohort

BackgroundAutism spectrum disorder (henceforth autism) is a complex neurodevelopmental condition associated with differences in gray matter (GM) volume covariations, as reported in our previous study of the Longitudinal European Autism Project (LEAP) data. To make progress on the identification of potential neural markers and to validate the robustness of our previous findings, we aimed to replicate our results using data from the Enhancing Neuroimaging Genetics Through Meta-Analysis (ENIGMA) autism working group.MethodsWe studied 781 autistic and 927 non-autistic individuals (6–30 years, IQ ≥ 50), across 37 sites. Voxel-based morphometry was used to quantify GM volume as before. Subsequently, we used spatial maps of the two autism-related independent components (ICs) previously identified in the LEAP sample as templates for regression analyses to separately estimate the ENIGMA-participant loadings to each of these two ICs. Between-group differences in participants’ loadings on each component were examined, and we additionally investigated the relation between participant loadings and autistic behaviors within the autism group.ResultsThe two components of interest, previously identified in the LEAP dataset, showed significant between-group differences upon regressions into the ENIGMA cohort. The associated brain patterns were consistent with those found in the initial identification study. The first IC was primarily associated with increased volumes of bilateral insula, inferior frontal gyrus, orbitofrontal cortex, and caudate in the autism group relative to the control group (β = 0.129, p = 0.013). The second IC was related to increased volumes of the bilateral amygdala, hippocampus, and parahippocampal gyrus in the autism group relative to non-autistic individuals (β = 0.116, p = 0.024). However, when accounting for the site-by-group interaction effect, no significant main effect of the group can be identified (p > 0.590). We did not find significant univariate association between the brain measures and behavior in autism (p > 0.085).LimitationsThe distributions of age, IQ, and sex between LEAP and ENIGMA are statistically different from each other. Owing to limited access to the behavioral data of the autism group, we were unable to further our understanding of the neural basis of behavioral dimensions of the sample.ConclusionsThe current study is unable to fully replicate the autism-related brain patterns from LEAP in the ENIGMA cohort. The diverse group effects across ENIGMA sites demonstrate the challenges of generalizing the average findings of the GM covariation patterns to a large-scale cohort integrated retrospectively from multiple studies. Further analyses need to be conducted to gain additional insights into the generalizability of these two GM covariation patterns.

Automatic detection of mild cognitive impairment based on deep learning and radiomics of MR imaging.

Early and rapid diagnosis of mild cognitive impairment (MCI) has important clinical value in improving the prognosis of Alzheimer's disease (AD). The hippocampus and parahippocampal gyrus play crucial roles in the occurrence of cognitive function decline. In this study, deep learning and radiomics techniques were used to automatically detect MCI from healthy controls (HCs). This study included 115 MCI patients and 133 normal individuals with 3D-T1 weighted MR structural images from the ADNI database. The identification and segmentation of the hippocampus and parahippocampal gyrus were automatically performed with a VB-net, and radiomics features were extracted. Relief, Minimum Redundancy Maximum Correlation, Recursive Feature Elimination and the minimum absolute shrinkage and selection operator (LASSO) were used to reduce the dimensionality and select the optimal features. Five independent machine learning classifiers including Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Bagging Decision Tree (BDT), and Gaussian Process (GP) were trained on the training set, and validated on the testing set to detect the MCI. The Delong test was used to assess the performance of different models. Our VB-net could automatically identify and segment the bilateral hippocampus and parahippocampal gyrus. After four steps of feature dimensionality reduction, the GP models based on combined features (11 features from the hippocampus, and 4 features from the parahippocampal gyrus) showed the best performance for the MCI and normal control subject discrimination. The AUC of the training set and test set were 0.954 (95% CI: 0.929-0.979) and 0.866 (95% CI: 0.757-0.976), respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high. The GP classifier based on 15 radiomics features of bilateral hippocampal and parahippocampal gyrus could detect MCI from normal controls with high accuracy based on conventional MR images. Our fully automatic model could rapidly process the MRI data and give results in 1 minute, which provided important clinical value in assisted diagnosis.

Facial emotion recognition deficits are associated with hypomimia and related brain correlates in Parkinson's disease.

Hypomimia is a frequent manifestation in Parkinson's disease (PD) that can affect interpersonal relationships and quality of life. Recent studies have suggested that hypomimia is not only related to motor dysfunction but also to impairment in emotional processing networks. Therefore, we hypothesized that the severity of hypomimia could be associated with performance on a task aimed at assessing facial emotion recognition. In this study, we explored the association between hypomimia, recognition of facial expressions of basic emotions using the Ekman 60 Faces Test (EF), and brain correlates of both hypomimia and performance on the EF. A total of 94 subjects underwent clinical assessments (neurological and neuropsychological examinations), and 56 of them participated in the neuroimaging study. We found significant correlation between hypomimia, EF Disgust (r = -0.242, p = 0.022) and EF Happiness (r = -0.264, p = 0.012); an independent reduction in Cortical Thickness (Cth) in the postcentral gyrus, insula, middle and superior temporal gyri, supramarginal gyrus, banks of the superior temporal sulcus, bilateral fusiform gyri, entorhinal cortex, parahippocampal gyrus, inferior and superior parietal cortex, and right cuneus and precuneus; and multiple correlations between negative emotions such as EF Disgust or EF Anger and a reduced Cth in fronto-temporo-parietal regions. In conclusion, these results suggest that the association between hypomimia and emotion recognition deficits in individuals with PD might be mediated by shared circuits, supporting the concept that hypomimia is not only the result of the dysfunction of motor circuits, but also of higher cognitive functions.

Neurostructural Consequences of Obstetric Brachial Plexus Palsy in Childhood.

Obstetric brachial plexus palsy (OBPP) is a condition impairing limb function caused by birth injury. In 20 to 30% of cases, severe OBPP can cause life constraints in feeding, grooming, and clothing tasks. The present study, using voxel- and surface-based morphometry (VBM and SBM), examined the brain structure of pediatric OBPP patients to better understand the effects of this peripheral motor deficit on early brain development. Thirty-six T1-weighted images of 18 patients (2-17 years old, mean age = 11.3, 8 females) and 18 healthy controls (2-17 years old, mean age = 10.1, 8 females) were collected for this study. MRI data were processed and analyzed using the Statistical Parametric Mapping 12 (SPM12) toolbox. The custom pediatric tissue probability map was created with the CerebroMatic (COM) toolbox. The results were considered significant if they survived whole-brain family-wise error correction (P < .05). We have found differences in grey matter volumes in the bilateral anterior hippocampus (left P < .001 and right P = .01) and left cerebellum exterior (Crus I) (P < .001). We have also found differences in cortical thickness in the bilateral parahippocampal gyri (left P = .001 and right P = .005) and right orbitofrontal cortex (OFC) (P < .001). These structural differences might be linked to the altered environmental adaptation that children with OBPP face due to their primary motor deficit. Our findings hint at a complex interplay between motor capabilities, brain structure development, and cognitive functions. However, more research combining neuroimaging, behavioral, cognitive, and clinical data is needed to support stronger conclusions on this subject.

Automatic MRI volumetry in asymptomatic cases at risk for normal pressure hydrocephalus.

The occurrence of significant Alzheimer's disease (AD) pathology was described in approximately 30% of normal pressure hydrocephalus (NPH) cases, leading to the distinction between neurodegenerative and idiopathic forms of this disorder. Whether or not there is a specific MRI signature of NPH remains a matter of debate. The present study focuses on asymptomatic cases at risk for NPH as defined with automatic machine learning tools and combines automatic MRI assessment of cortical and white matter volumetry, risk of AD (AD-RAI), and brain age gap estimation (BrainAge). Our hypothesis was that brain aging and AD process-independent volumetric changes occur in asymptomatic NPH-positive cases. We explored the volumetric changes in normal aging-sensitive (entorhinal cortex and parahippocampal gyrus/PHG) and AD-signature areas (hippocampus), four control cortical areas (frontal, parietal, occipital, and temporal), and cerebral and cerebellar white matter in 30 asymptomatic cases at risk for NPH (NPH probability >30) compared to 30 NPH-negative cases (NPH probability <5) with preserved cognition. In univariate regression models, NPH positivity was associated with decreased volumes in the hippocampus, parahippocampal gyrus (PHG), and entorhinal cortex bilaterally. The strongest negative association was found in the left hippocampus that persisted when adjusting for AD-RAI and Brain Age values. A combined model including the three parameters explained 36.5% of the variance, left hippocampal volumes, and BrainAge values, which remained independent predictors of the NPH status. Bilateral PHG and entorhinal cortex volumes were negatively associated with NPH-positive status in univariate models but this relationship did not persist when adjusting for BrainAge, the latter remaining the only predictor of the NPH status. We also found a negative association between bilateral cerebral and cerebellar white matter volumes and NPH status that persisted after controlling for AD-RAI or Brain Age values, explaining between 50 and 65% of its variance. These observations support the idea that in cases at risk for NPH, as defined by support vector machine assessment of NPH-related MRI markers, brain aging-related and brain aging and AD-independent volumetric changes coexist. The latter concerns volume loss in restricted hippocampal and white matter areas that could be considered as the MRI signature of idiopathic forms of NPH.

The remodeling of metabolic brain pattern in patients with extracranial diffuse large B-cell lymphoma

BackgroundOwing to the advances in diagnosis and therapy, survival or remission rates for lymphoma have improved prominently. Apart from the lymphoma- and chemotherapy-related somatic symptom burden, increasing attention has been drawn to the health-related quality of life. The application of 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) has been routinely recommended for the staging and response assessment of FDG-avid lymphoma. However, up till now, only a few researches have investigated the brain metabolic impairments in patients with pre-treatment lymphoma. The determination of the lymphoma-related metabolic brain pattern would facilitate exploring the tailored therapeutic regimen to alleviate not only the physiological, but also the psychological symptoms. In this retrospective study, we aimed to establish the diffuse large B-cell lymphoma-related pattern (DLBCLRP) of metabolic brain network and investigate the correlations between DLBCLRP and several indexes of the staging and response assessment.ResultsThe established DLBCLRP was characterized by the increased metabolic activity in bilateral cerebellum, brainstem, thalamus, striatum, hippocampus, amygdala, parahippocampal gyrus and right middle temporal gyrus and by the decreased metabolic activity in bilateral occipital lobe, parietal lobe, anterior cingulate gyrus, midcingulate cortex and medial frontal gyrus. Significant difference in the baseline expression of DLBCLRP was found among complete metabolic response (CMR), partial metabolic response (PMR) and progressive metabolic disease (PMD) groups (P < 0.01). DLBCLRP expressions were also significantly or tended to be positively correlated with international prognostic index (IPI) (rs = 0.306, P < 0.05), lg(total metabolic tumor volume, TMTV) (r = 0.298, P < 0.05) and lg(total lesion glycolysis, TLG) (r = 0.233, P = 0.064). Though no significant correlation of DLBCLRP expression was found with Ann Arbor staging or tumor SUVmax (P > 0.05), the post-treatment declines of DLBCLRP expression were significantly positively correlated with Ann Arbor staging (rs = 0.284, P < 0.05) and IPI (rs = 0.297, P < 0.05).ConclusionsThe proposed DLBCLRP would lay the foundation for further investigating the cerebral dysfunction related to DLBCL itself and/or treatments. Besides, the expression of DLBCLRP was associated with the tumor burden of lymphoma, implying a potential biomarker for prognosis.

Dynamic alterations in the amplitude of low-frequency fluctuation in patients with cerebral small vessel disease.

Previous studies have focused on the changes of dynamic and static functional connections in cerebral small vessel disease (CSVD). However, the dynamic characteristics of local brain activity are poorly understood. The purpose of this study was to investigate the dynamic cerebral activity changes in patients with CSVD using the dynamic amplitude of low-frequency fluctuation (d-ALFF). A total of 104 CSVD patients with cognitive impairment (CSVD-CI, n = 52) or normal cognition (CSVD-NC, n = 52) and 63 matched healthy controls (HCs) were included in this study. Every participant underwent magnetic resonance imaging scans and a battery of neuropsychological examinations. The dynamics of spontaneous brain activity were assessed using dynamic changes in the amplitude of low-frequency fluctuation (ALFF) with the sliding-window method. We used voxel-wise one-way analysis of variance (ANOVA) to compare dynamic ALFF variability among the three groups. Post-hoc t-tests were used to evaluate differences between each group pair. Finally, the brain regions with d-ALFF values with differences between CSVD subgroups were taken as regions of interest (ROI), and the d-ALFF values corresponding to the ROI were extracted for partial correlation analysis with memory. (1) There was no significant difference in age (p = 0.120), sex (p = 0.673) and education (p = 0.067) among CSVD-CI, CSVD-NC and HC groups, but there were significant differences Prevalence of hypertension and diabetes mellitus among the three groups (p < 10-3). There were significant differences in scores of several neuropsychological scales among the three groups (p < 10-3). (2) ANOVA and post-hoc t-test showed that there were dynamic abnormalities of spontaneous activity in several brain regions in three groups, mainly located in bilateral parahippocampal gyrus and bilateral hippocampus, bilateral insular and frontal lobes, and the static activity abnormalities in bilateral parahippocampal gyrus and bilateral hippocampal regions were observed at the same time, suggesting that bilateral parahippocampal gyrus and bilateral hippocampus may be the key brain regions for cognitive impairment caused by CSVD. (3) The correlation showed that d-ALFF in the bilateral insular was slightly correlated with the Mini-Mental State Examination (MMSE) score and disease progression rate. The d-ALFF value of the left postcentral gyrus was negatively correlated with the Clock Drawing Test (CDT) score (r = -0.416, p = 0.004), and the d-ALFF value of the right postcentral gyrus was negatively correlated with the Rey's Auditory Verbal Learning Test (RAVLT) word recognition (r = -0.320, p = 0.028). There is a wide range of dynamic abnormalities of spontaneous brain activity in patients with CSVD, in which the abnormalities of this activity in specific brain regions are related to memory and execution or emotion.

Functional changes in the salience network of patients with amnesic mild cognitive impairment before and after repetitive transcranial magnetic stimulation.

Amnestic mild cognitive impairment (aMCI) is considered to be the prodromal stage of Alzheimer's disease (AD). The precuneus (PCUN) may be an imaging marker for monitoring the progression of AD. Meanwhile, cognitive impairment in AD patients is closely related to functional connectivity (FC) changes in the salience network (SN). We hypothesize that there are specific neuroimaging biomarkers in the SN and that FC changes in aMCI patients after repetitive transcranial magnetic stimulation (rTMS) intervention are associated with cognitive function. The purpose of this study was to first investigate the pattern of functional changes in aMCI patients and healthy controls (HCs) and then compare the functional changes in aMCI patients before and after rTMS targeting to PCUN and its correlation with cognitive function. Thirty-six HCs and 61 aMCIs were recruited for our study. Eleven people in the aMCI group received rTMS intervention 5 days a week for 4 weeks. Using the right anterior insula as the seed-of-interest, we first compared FC changes in HC and aMCI patients and then compared cognitive function in aMCI patients before and after rTMS. The above is the functional connection analysis of seed-to-voxel. Moreover, we investigated the FC changes in aMCI patients after rTMS intervention and its correlation with cognitive function. Compared with HC, the aMCI group showed altered FC in bilateral parahippocampal gyrus, bilateral inferior parietal lobule, left middle frontal gyrus, and left middle temporal gyrus. Moreover, rTMS at PCUN improved the cognitive function of aMCI patients, which was related to the altered FC in posterior cerebellar lobes (CPL). Our findings suggest that rTMS targeting PCUN is a promising, noninvasive approach to ameliorating cognitive dysfunction in aMCI patients, and that this cognitive improvement is accompanied by brain connectivity modulation.

Brain Connectome Imaging Markers Research of Glucose Metabolism in the Early Stage of Alzheimer's Disease.

In recent years, increasing evidence had suggested that subjective cognitive decline (SCD) in unimpaired individuals may be the first symptom of Alzheimer's disease (AD). This study investigated the differences in the glucose metabolism network and the influence of the Apolipoprotein E (ApoE) gene between the SCD and normal control (NC) group by using graph theory. In this study, we included 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scans from Xuanwu Hospital in Beijing, China. 85 SCD subjects and 74 NC subjects were included. First, we calculated and compared network parameters between the two groups. We then identified the bilateral insula and bilateral parahippocampal gyrus as seed sites and studied the connections to the whole brain. The results showed that both the SCD and the NC showed small-world nature, but the metabolic network of SCD tended to be more regular. The clustering coefficient and local efficiency of SCD were significantly higher than those of NC (P<0.05). In addition, we found that carrying APOE resulted in enhanced metabolic connectivity, but with weaker aggregation and local information exchangeability. Our results suggested that there are differences in the glucose metabolic brain network between SCD and NC, suggesting that the graph-theoretic analysis method may provide evidence for the early pathological mechanism of AD.Clinical relevance- This study suggests that the graph-theoretic analysis method may provide evidence for the early pathological mechanism of AD.

Coupling Between Hippocampal Parenchymal Fraction and Cortical Grey Matter Atrophy at Different Stages of Cognitive Decline.

Hippocampal atrophy is a significant brain marker of pathology in Alzheimer's disease (AD). The hippocampal parenchymal fraction (HPF) was recently developed to better assess the hippocampal volumetric integrity, and it has been shown to be a sensitive measure of hippocampal atrophy in AD. To investigate the clinical relevance of hippocampal volumetric integrity as measured by the HPF and the coupling between the HPF and brain atrophy during AD progression. We included data from 143 cognitively normal (CN), 101 mild cognitive impairment (MCI), and 125 AD participants. We examined group differences in the HPF, associations between HPF and cognitive ability, and coupling between the HPF and cortical grey matter volume in the CN, MCI, and AD groups. We observed progressive decreases in HPF from CN to MCI and from MCI to AD, and increases in the asymmetry of HPF, with the lowest asymmetry index (AI) in the CN group and the highest AI in the AD group. There was a significant association between HPF and cognitive ability across participants. The coupling between HPF and cortical regions was observed in bilateral hippocampus, parahippocampal gyrus, temporal, frontal, and occipital regions, thalamus, and amygdala in CN, MCI, and AD groups, with a greater involvement of temporal, occipital, frontal, and subcortical regions in MCI and AD patients, especially in AD patients. This study provides novel evidence for the neuroanatomical basis of cognitive decline and brain atrophy during AD progression, which may have important clinical implications for the prognosis of AD.

Voxel-based morphometry reveals the correlation between gray matter volume and serum P-tau-181 in type 2 diabetes mellitus patients with different HbA1c levels.

Emerging evidence suggested widespread decreased gray matter volume (GMV) and tau hyperphosphorylation were associated with type 2 diabetes mellitus (T2DM). Insulin resistance is one of the mechanisms of neuron degeneration in T2DM; it can decrease the activity of protein kinase B and increase the activity of glycogen synthesis kinase-3β, thus promoting the hyperphosphorylation of tau protein and finally leading to neuronal degeneration. However, the association between GMV and serum tau protein phosphorylated at threonine 181 (P-tau-181) in T2DM patients lacks neuroimaging evidence. We aimed to investigate the difference in brain GMV between T2DM patients with different glycated hemoglobin A1c (HbA1c) levels and healthy control (HC) subjects and the correlation between serum P-tau-181 and GMV in T2DM patients. Clinical parameters, biochemical indicators, and MRI data were collected for 41 T2DM patients with high glycosylated hemoglobin level (HGL), 17 T2DM patients with normal glycosylated hemoglobin level (NGL), and 42 HC subjects. Voxel-based morphometry (VBM) method was applied to investigate GMV differences among groups, and multiple regression analysis was used to examine the correlation between serum P-tau-181 and GMV. Compared with HC subjects, the T2DM patients with HGL or NGL all showed significantly decreased GMV. Briefly, the GMV decreased in T2DM patients with HGL was mainly in the bilateral parahippocampal gyrus (PHG), right middle temporal gyrus (MTG), temporal pole (TPOmid), hippocampus (HIP), and left lingual gyrus. The GMV reduction in T2DM patients with NGL was in the right superior temporal gyrus (STG), and there was no significant difference in GMV between the two diabetic groups. The GMV values of bilateral PHG, right MTG, TPOmid, HIP, and STG can significantly (p < 0.0001) distinguish T2DM patients from HC subjects in ROC curve analysis. In addition, we found that serum P-tau-181 levels were positively correlated with GMV in the right superior and middle occipital gyrus and cuneus, and negatively correlated with GMV in the right inferior temporal gyrus in T2DM patients. Our study shows that GMV atrophy can be used as a potential biological indicator of T2DM and also emphasizes the important role of P-tau-181 in diabetic brain injury, providing new insights into the neuropathological mechanism of diabetic encephalopathy.