Background:Three subgroups of mild cognitive impairment (MCI) may be identified based on the deposition of Aβ and tau proteins: A-T-, A + T+, and A + T-. The key hub for information processing and control, the anterior cingulate cortex (ACC), is essential for both healthy aging and MCI. The objective of this research is to systematically investigate changes in the functional connectivity (FC) of ACC subregions across different MCI subtypes. Methods:Overall, 54 A-T- patients, 28 A + T- patients, and 52 A + T + patients underwent FC analysis of ACC subregions. Correlation analyses were conducted to explore the relationship among pathological biomarkers, cognitive function, and FC changes in ACC subnetworks. The diagnostic utility of ACC-cortical FC in differentiating MCI subtypes was evaluated using receiver operating characteristic (ROC) curves. Results:Compared with the A-T- group, the A + T- group demonstrated reduced FC in the right precuneus and left dorsal ACC, whereas the A + T + group demonstrated increased FC in the left hippocampus, right precuneus and left dorsal ACC, the left superior frontal gyrus and right subgenual ACC. Compared with the A + T- group, the A + T + group demonstrated increased FC in the right precuneus and left dorsal ACC. The altered FC in ACC subnetworks was significantly correlated with pathological biomarkers in the cerebrospinal fluid. The ROC curve analysis suggested that changes in ACC FC effectively distinguished between the various pathological subtypes of MCI. Conclusion:In summary, different MCI subtypes have distinctive changes in the FC of ACC subregions, providing valuable insights into the mechanisms underlying MCI.

BackgroundMild cognitive impairment (MCI) is the prodromal stage of Alzheimer’s disease (AD), the primary cause of dementia. In addition to supporting motor and higher-order cognitive activities, the basal ganglia, particularly the caudate nucleus and putamen, may exhibit early AD-related network changes. This study investigated differences in striatal functional connectivity (FC) across MCI subtypes marked by cerebrospinal fluid (CSF) amyloid-β (Aβ42) and phosphorylated tau (p-tau) (A/T) biomarkers (A+/A- and T+/T-), along with associations among altered FC, AD pathology, and cognitive performance.MethodsFrom the ADNI database, 212 individuals with MCI were stratified into three groups: A-T- (n = 54), A+T- (n = 28), and A+T+ (n = 52). Group differences in putamen and caudate connectivity were investigated using seed-based FC analyses. Changes in FC, CSF biomarkers, and cognitive function were evaluated using partial correlation analyses. The discriminative value of FC changes was assessed using univariate and multivariate logistic regression analyses.ResultsCompared with the A-T- and A+T- groups, the A+T+ group was older and showed lower episodic memory (EM) scores. The left caudate and bilateral cerebellar anterior lobes, between the right caudate and bilateral medial frontal gyrus (MFG), and between the left putamen and left MFG all had higher FC in the A+T+ group. In the A+T+ group, right caudate–MFG connectivity was positively correlated with CSF p-tau levels (r = 0.424, p = 0.012) and negatively correlated with EM scores (r = -0.38, p = 0.018). Compared with single-region models, multivariate logistic regression demonstrated superior classification performance.ConclusionPatients with MCI and coexisting CSF Aβ and tau pathologies (A+T+) exhibit increased FC in striatal-cortical circuits, which is strongly associated with tau pathology and episodic memory impairment. These findings provide neuroimaging evidence that striatal network alterations are linked to tau-related pathology in prodromal AD. Changes in striatal FC may serve as an early neurobiological indicator of AD-related MCI.

To evaluate the clinical value of three-dimensional time-of-flight magnetic resonance angiography (3D TOF-MRA), three-dimensional fast imaging employing steady-state acquisition with phase cycling (3D FIESTA-c), and their combination for preoperative assessment of responsible vessels in primary trigeminal neuralgia (PTN) before microvascular decompression (MVD). We retrospectively reviewed 412 patients with PTN confirmed by MVD who underwent preoperative 3D TOF-MRA and 3D FIESTA-c. Intraoperative findings served as the reference standard. Diagnostic performance was assessed using 2 criteria: binary identification of the presence or absence of a responsible vessel and complete concordance of vessel type. Agreement with surgical findings was evaluated using Cohen’s kappa. Subgroup analyses were performed according to responsible vessel type. Responsible vessels were identified intraoperatively in 407 of 412 patients (98.79

Background Patients with treatment-resistant schizophrenia (TRS) experience more severe clinical symptoms, which may derive from greater structural and functional brain abnormalities compared to non-TRS. However, the neural underpinnings of TRS remain poorly understood, and conventional unimodal neuroimaging approaches are limited in capturing cross-modal interactions. Therefore, we used multimodal fusion via multiset canonical correlation and joint independent component analysis (mCCA+jICA) to integrate MRI data to examine shared and modality-specific features in TRS.Methods65 TRS patients, 65 non-TRS patients, and 54 healthy controls (HCs) underwent 3 T MRI. Structural MRI and resting-state functional MRI data were preprocessed using DPARSFA, and grey matter volume (GMV), fractional amplitude of low-frequency fluctuations (fALFF), and regional homogeneity (ReHo) maps were extracted. The mCCA+jICA was performed to derive joint independent components (ICs) and subject-specific mixing coefficients. Group differences in mixing coefficients were assessed via MANCOVA, and clinical correlations were evaluated using partial correlations. Results Patients showed significant differences in mixing coefficients for one modality-specific group-discriminative IC (GMV-IC3) and two joint group-discriminative ICs (ReHo-IC6, and GMV-IC6) compared to HCs. These components also differentiated TRS from non-TRS, with abnormalities concentrated in the precuneus in both ReHo and GMV. Partial correlation analysis revealed significant positive associations between the mixing coefficients of GMV-IC3, GMV-IC6, and total PANSS scores and CPZ equivalents specifically in the TRS group. Conclusions This study demonstrates that co-existing structural and functional brain alterations are associated with the pathophysiology of TRS, highlighting their potential as novel multimodal biomarkers.

Background:Alzheimer's disease (AD) pathology begins years before clinical symptoms, with Mild Cognitive Impairment (MCI) as a prodromal stage. The ATN framework (Amyloid, Tau, Neurodegeneration) aids in stratifying MCI risk. While amygdala atrophy is a recognized biomarker, amygdala subregional changes across ATN-defined MCI subgroups remain underexplored. Methods:This study analyzed MRI data and cerebrospinal fluid biomarkers from 134 MCI participants classified into A-T-, A+T-, and A+T+ subgroups. The volumes of amygdala subregions were computed and compared among the different groups. Furthermore, we also investigated the relationship between the altered brain regions and cognitive function. Results:Significant atrophy was observed in the A+T+ group within bilateral basal, accessory basal, central nuclei, and right cortical-amygdaloid transition area compared to other groups. Volume reductions in the left central nucleus correlated positively with cognitive scores. Conclusion:Amygdala subregional atrophy, particularly in the central, basal, accessory basal, and cortical-amygdaloid transition nuclei, is linked to AD pathology progression and cognitive decline. The findings suggest a potential vulnerability of the right amygdala and suggest these subregions may be associated with AD-related pathological progression.

Background: Moyamoya angiopathy (MMA) can be potentially missed in the initial magnetic resonance (MR) examination without MR angiography (MRA). The aim of this study was to develop an optimal deep learning model based on T2-weighted imaging (T2WI) for MMA detection. Methods: This retrospective multicenter study included MMA patients, control group patients with normal MRA and patients with cerebrovascular disease except MMA from seven hospitals (site 1 to site 7). Convolutional Network (DenseNet), were used for training and validation. The model training and internal validation were performed with data from sites 1-4. Data from sites 5-7 were used for independent external validation, and the optimal model was selected according to the results of accuracy. Chi-squared test was used to further verify the influence of different MR manufacturers, field strength, age at the MR examination and MRA score on the optimal model. Results: A total of 1,038 MMA patients, 1,211 normal MRA and 271 patients with cerebrovascular disease except MMA were included. DenseNet showed the highest accuracy (0.859, 95% CI: 0.833, 0.884) in the independent external validation, which was not significantly different from that of VGG (0.834, 95% CI: 0.807, 0.861) and ResNet (0.855, 95% CI: 0.829, 0.880) but was significantly higher than that of SCNN (0.631, 95% CI: 0.595, 0.665; P<0.001) and LeNet (0.563, 95% CI: 0.527, 0.599; P=0.001). The accuracy of 1.5 T data was higher than 3.0 T data (chi 2=6.559, P=0.01). The accuracy of MMA with MRA who scored more than 5 was higher than that scoring <= 5 (<= 5 vs. 6-10: chi 2=10.734, P=0.001; <= 5 vs. >= 11: chi 2=10.369, VGG and ResNet. The MRA score of MMA affected the DenseNet accuracy.

Background:The insula is a critical node of the salience network responsible for initiating network switching, and its dysfunctional connections are linked to the mechanisms of mild cognitive impairment (MCI). This study aimed to explore the changes in functional connectivity (FC) of insular subregions in MCI patients with varying levels of cerebrospinal fluid (CSF) pathological proteins, and to investigate the impact of these proteins on the brain network alterations in MCI. Methods:Based on CSF Amyloid-beta (Aβ, A) and phosphorylated tau protein (p-tau, T), MCI patients were classified into 54 A-T-, 28 A+T-, and 52 A+T+ groups. Seed-based FC analysis was employed to compare the FC differences of insular subregions across the three groups. Correlation analysis was further conducted to explore the relationship between altered FC and cognitive function. Finally, ROC curve analysis was used to assess the diagnostic value of altered FC of insular subregion in distinguishing between the groups. Results:In the left ventral anterior insula, left dorsal anterior insula, and bilateral posterior insular subnetworks, both the A+T- and A+T+ groups showed increased FC compared to the A-T- group, with the A+T+ group showing further increased FC compared to the A+T- group. Additionally, FC of the left cerebellar posterior lobe was negatively correlated with RAVLT-learning, and FC of the left middle frontal gyrus was negatively correlated with p-tau levels. Finally, logistic regression analysis demonstrated that multivariable analysis had high sensitivity and specificity in distinguishing between the groups. Conclusion:This study showed that MCI patients with abnormal CSF pathological protein levels exhibit compensatory increases in FC of insular subregions, which in turn affect cognitive function. Our findings contributed to a better understanding of the pathophysiology and underlying neural mechanisms of MCI.

Sleep dysfunction (SD) is common in Alzheimer's disease (AD) and associated with cognitive impairment. The hypothalamus regulates circadian rhythms and exhibits AD pathology; we investigated the abnormal alterations in the structure and function of the hypothalamus in AD patients with sleep dysfunction (ADSD). Twelve ADSD patients, 19 AD patients without sleep dysfunction (ADNSD), and 13 age- and sex-matched healthy controls (HCs) underwent neuropsychological assessments, including the Montreal Cognitive Assessment and Mini-Mental State Examination, and sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI). Hypothalamic volume and its subregional volumes were derived from T1-weighted magnetic resonance imaging (MRI), whereas resting-state functional MRI was used to assess functional connectivity (FC) with the hypothalamus. We found reduced volumes in the bilateral anterior inferior and left anterior superior hypothalamic subregions in both AD groups compared to HCs. Compared to HCs, the ADNSD group exhibited enhanced hypothalamic FC with the left middle frontal gyrus (MFG) and right inferior parietal lobule (IPL) and reduced FC with right MFG, whereas the ADSD group exhibited enhanced FC with the left MFG and right superior frontal gyrus and reduced FC with right IPL. Furthermore, the ADSD group exhibited reduced FC with the right IPL and enhanced FC with right MFG relative to the ADNSD group. Crucially, within the combined AD cohort, FC with the right IPL was negatively correlated with PSQI, whereas FC with the right MFG was positively correlated. These findings indicate the hypothalamus is a critical target for interventions to improve sleep quality and cognition in AD.

Objective:This study aims to evaluate the effectiveness of Magnetic Resonance Virtual Endoscopy combined with 3D-FIESTA-c and 3D-TOF-MRA in preoperative assessment of MVD for PTN, with a focus on accurately detecting neuromuscular contact. Methods:We retrospectively analyzed clinical and imaging data from 240 patients with unilateral primary trigeminal neuralgia undergoing MVD surgery between April 2016 and July 2023. Preoperative scans with 3D-FIESTA-c and 3D-TOF-MRA were performed, and MRVE images were obtained to analyze the relationship between the trigeminal nerve and adjacent vessels. Using the findings during microvascular decompression (MVD) surgery as the gold standard, the diagnostic results of 3D-TOF-MRA + 3D-FIESTA-c were considered as group I, while the combined use of MRVE, 3D-TOF-MRA + 3D-FIESTA-c was considered as group II. Results:In 240 cases, group I had a positive rate of 96.25% and an accuracy rate of 86.25% for identifying responsible blood vessels, while group II had a positive rate of 98.3% and an accuracy rate of 94.17%. There were no statistically significant differences in positive rates between group I and group II, group I and MVD, or group II and MVD (P > 0.05). However, there were statistically significant differences in accuracy rates (P < 0.05). The accuracy for single and multiple arteries with group I was 99.38% and 80.0%, respectively, while with group II, it was 100% and 95.0%. No statistically significant difference was found in accuracy for single or multiple arteries (P>0.05). The accuracy of evaluating responsibility veins with or without other vessels was 52.73% and 80.0%, respectively, with a statistically significant difference (P<0.05). Conclusion:MRVE combined with 3D-TOF-MRA + 3D-FIESTA-c significantly improves the accuracy of identifying responsibility vessels, especially veins, in preoperative assessment for MVD. This has important clinical implications for preoperative decision-making and surgical planning.

ObjectivesSubjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are considered as the spectrum of preclinical Alzheimer’s disease (AD), with abnormal brain network connectivity as the main neuroimaging feature. Repetitive transcranial magnetic stimulation (rTMS) has been proven to be an effective non-invasive technique for addressing neuropsychiatric disorders. This study aims to explore the potential of targeted rTMS to regulate effective connectivity within the default mode network (DMN) and the executive control network (CEN), thereby improving cognitive function.MethodsThis study included 86 healthy controls (HCs), 72 SCDs, and 86 aMCIs. Among them, 10 SCDs and 11 aMCIs received a 2-week rTMS course of 5-day, once-daily. Cross-sectional analysis with the spectral dynamic causal model (spDCM) was used to analyze the DMN and CEN effective connectivity patterns of the three groups. Afterwards, longitudinal analysis was conducted on the changes in effective connectivity patterns and cognitive function before and after rTMS for SCD and aMCI, and the correlation between them was analyzed.ResultsCross-sectional analysis showed different effective connectivity patterns in the DMN and CEN among the three groups. Longitudinal analysis showed that the effective connectivity pattern of the SCD had changed, accompanied by improvements in episodic memory. Correlation analysis indicated a negative relationship between effective connectivity from the left angular gyrus (ANG) to the anterior cingulate gyrus and the ANG.R to the right middle frontal gyrus, with visuospatial and executive function, respectively. In patients with aMCI, episodic memory and executive function improved, while the effective connectivity pattern remained unchanged.ConclusionThis study demonstrates that PCUN-targeted rTMS in SCD regulates the abnormal effective connectivity patterns in DMN and CEN, thereby improving cognition function. Conversely, in aMCI, the mechanism of improvement may differ. Our findings further suggest that rTMS is more effective in preventing or delaying disease progression in the earlier stages of the AD spectrum.Clinical Trial Registrationhttp://www.chictr.org.cn, ChiCTR2000034533.

Rationale and Objectives Recent radiomics studies on predicting pathological outcomes of glioma have shown immense potential. However, the predictive ability remains suboptimal due to the tumor intrinsic heterogeneity. We aimed to achieve better pathological prediction outcomes by combining habitat analysis with deep learning. Materials and Methods 387 cases of primary glioma from three hospitals were collected, along with their T1 contrast-enhanced and T2-weighted MR sequences, pathological reports and clinical histories. The training set consisted of 264 patients, 82 patients composed the test set, and 41 patients were used as the validation set for hyperparameter tuning and optimal model selection. All groups were sourced from different centers. Through radiomics, deep learning, habitat analysis and combined analysis, we extracted imaging features separately and jointly modeled them with clinical features. We identified the optimal models for predicting glioma grades, Ki67 expression levels, P53 mutation and IDH1 mutation. Results Using a LightGBM model with DenseNet161 features based on habitat subregions, the best tumor grade prediction model was achieved. A LightGBM model with ResNet50 features based on habitat subregions yielded the best Ki67 expression level prediction model. An SVM model with Radiomics and Inception_v3 features provided the best prediction of P53 mutation. The best model for predicting IDH1 mutation was achieved by an MLP model with Radiomics features based on habitat subregions. Clinical features might be potentially helpful for the prediction with relatively weak evidence. Conclusion Habitat+Deep Learning feature extraction methods were optimal for predicting grades and Ki67 levels. Deep Learning is optimal for predicting P53 mutation, while the combination of Habitat+ Radiomics models yielded the best prediction for IDH1 mutation.

Background: Mild cognitive impairment (MCI), the prodromal stage of Alzheimer’s disease, has two distinct subtypes: stable MCI (sMCI) and progressive MCI (pMCI). Early identification of the two subtypes has important clinical significance. Objective: We aimed to compare the cortico-striatal functional connectivity (FC) differences between the two subtypes of MCI and enhance the accuracy of differential diagnosis between sMCI and pMCI. Methods: We collected resting-state fMRI data from 31 pMCI patients, 41 sMCI patients, and 81 healthy controls. We chose six pairs of seed regions, including the ventral striatum inferior, ventral striatum superior, dorsal-caudal putamen, dorsal-rostral putamen, dorsal caudate, and ventral-rostral putamen and analyzed the differences in cortico-striatal FC among the three groups, additionally, the relationship between the altered FC within the MCI subtypes and cognitive function was examined. Results: Compared to sMCI, the pMCI patients exhibited decreased FC between the left dorsal-rostral putamen and right middle temporal gyrus, the right dorsal caudate and right inferior temporal gyrus, and the left dorsal-rostral putamen and left superior frontal gyrus. Additionally, the altered FC between the right inferior temporal gyrus and right putamen was significantly associated with episodic memory and executive function. Conclusions: Our study revealed common and distinct cortico-striatal FC changes in sMCIs and pMCI across different seeds; these changes were associated with cognitive function. These findings can help us understand the underlying pathophysiological mechanisms of MCI and distinguish pMCI and sMCI in the early stage potentially.

Background: This research aimed to delve into the cortical morphological transformations in patients with magnetic resonance imaging (MRI)-negative temporal lobe epilepsy (TLE-N), seeking to uncover the neuroimaging mechanisms behind these changes. Methods: A total of 29 individuals diagnosed with TLE-N and 30 healthy control participants matched by age and sex were selected for the study. Using the surface-based morphometry (SBM) technique, the study analyzed the three-dimensional-T1-weighted MRI scans of the participants' brains. Various cortical structure characteristics, such as thickness, surface area, volume, curvature, and sulcal depth, among other parameters, were measured. Results: When compared with the healthy control group, the TLE-N patients exhibited increased insular cortex thickness in both brain hemispheres. Additionally, there was a notable reduction in the curvature of the piriform cortex (PC) and the insular granular complex within the right hemisphere. In the left hemisphere, the volume of the secondary sensory cortex (OP1/SII) and the third visual area was significantly reduced in the TLE-N group. However, no significant differences were found between the groups regarding cortical surface area and sulcal depth (p < 0.025 for all, corrected by threshold-free cluster enhancement). Conclusions: The study's initial findings suggest subtle morphological changes in the cerebral cortex of TLE-N patients. The SBM technique proved effective in identifying brain regions impacted by epileptic activity. Understanding the microstructural morphology of the cerebral cortex offers insights into the pathophysiological mechanisms underlying TLE.

Objective: To observe the radiological characteristics of Neuronal Intranuclear Inclusion Disease (NIID) on lesion locations and diffusion property using quantitative imaging analysis. Methods: Visual inspection and quantitative analyses were performed on MRI data from 31 retrospectively included patients with NIID. Frequency heatmaps of lesion locations on T2WI and DWI were generated using voxel-wise analysis. Gray matter volume (GMV), white matter volume (WMV) and diffusion property of apparent diffusion coefficient (ADC) values of patients were voxel-wisely compared with healthy controls. Moreover, the ADC values within the DWI-detected lesion were compared with those within the adjacent cortical gray matter and white matter. Voxel-based lesion symptom mapping (VLSM) techniques, were used to determine the relationship between DWI lesion location and disease durations. Results: By visual inspection on the imaging findings, we proposed an "cockscomb flower sign" for describing the radiological feature of DWI hyperintensity within the corticomedullary junction. A "T2WI-DWI mismatch of spatial distribution" pattern was also revealed with visual inspection and frequency heatmaps, for describing the feature of a wider lesion distribution covering white matter shown on T2WI than that on DWI. Voxelbased morphometry comparison revealed that wildly reduced GMV and WMV, both the lesion areas detected by DWI and T2WI demonstrated ADC increase in patients. Furthermore, the ADC values within the DWIdetected lesion were intermediate between the adjacent cortex and the deep white matter with highest ADC. VLSM analysis revealed that frontal lobe, parietal lobe and internal capsule damage were associated with higher NIID durations. Conclusion: NIID features with "cockscomb flower-like" DWI hyperintensity in area of corticomedullary junction, based on a "T2WI-DWI mismatch of spatial distribution" of lesion locations. The pathological substrate of corticomedullary junction hyperintensity on DWI, can not be explained as diffusion restriction. These typical radiological features of brain MRI would be helpful for diagnosis of NIID. (c) 2023 Elsevier Masson SAS. All rights reserved.

BACKGROUND:Although symptoms of depressive episodes in patients with bipolar depressive episodes (BDE) and major depressive disorder (MDD) are similar, the treatment strategies for these disorders are completely different, suggesting that BDE and MDD have different neurobiological backgrounds. In this study, we examined the relationship between brain function and clinical symptoms, particularly cognitive function, in female individuals with bipolar disorder and MDD experiencing depressive episodes. METHODS:Regional homogeneity (ReHo) was analyzed in 51 medication-free female patients with BDE, 63 medication-free female patients with MDD, and 45 female healthy controls (HCs). Depressive symptom severity was assessed using the 24-item Hamilton Depression Rating Scale (HAMD-24), and multidimensional cognitive function was evaluated using the MATRICS Consensus Cognition Battery. Partial correlation analysis was used to explore the links between the brain regions and clinical characteristics. A support vector machine (SVM) was used to assess the classification accuracy. RESULTS:Compared with HCs, patients with BDE and MDD had decreased ReHo in the left lobule VI of the cerebellum and increased ReHo in the left precuneus. Patients with BDE also had reduced ReHo in the left lobules IV-V of the cerebellum and increased ReHo in the right putamen, unlike patients with MDD who had no significant differences in these regions. Patients with BDE exhibited more severe cognitive deficits in processing speed, attention, word learning, and overall cognitive function than those with MDD. In patients with BDE, a significant negative correlation was found between the right putamen and HAMD-24 scores. However, no significant association was observed between abnormal ReHo levels and cognitive function. The SVM effectively differentiated between patients with BDE, MDD, and HCs. CONCLUSION:Cognitive impairment was more severe in female patients with BDE than in those with MDD. Changes in the ReHo values of the right putamen and left lobules IV-V may serve as unique neuroimaging markers for BDE. Alterations in the ReHo values of the left precuneus and left lobule VI could serve as common pathophysiological mechanisms for BDE and MDD in women and indicate depressive states.

To characterize the structural plasticity of the contralesional hippocampus and its subfields in patients with unilateral glioma. 3D T1-weighted MRI images were collected from 55 patients with tumors infiltrating the left (HipL, n = 27) or right (HipR, n = 28) hippocampus, along with 30 age- and sex-matched healthy controls (HC). Gray matter volume differences of the contralesional hippocampal regions and three control regions (superior frontal gyrus, caudate nucleus, and superior occipital gyrus) were evaluated using voxel-based morphometry (VBM) analyses. Volumetric differences in the hippocampus and its subregional volume were measured using the FreeSurfer software. Compared with HC, patients with unilateral hippocampal glioma exhibited significantly larger gray matter volume in the contralesional hippocampus and parahippocampal regions (cluster = 571 voxels for HipL; cluster 1 = 538 voxels and cluster 2 = 88 voxels for HipR; family-wise error corrected p < 0.05). No significant alterations were found in control regions. Volumetric analyses showed the same trend in the contralesional hippocampal subregions for both patient groups, including the CA1 head, CA3 head, hippocampus amygdala transition area (HATA), fimbria, and the granule cell molecular layer of the dentate gyrus head (GC-ML-DG head). Notably, the differences of the contralesional HATA (HipL: η2 = 0.418, corrected p = 0.002; HipR: η2 = 0.313, corrected p = 0.052) and fimbria (HipL: η2 = 0.450, corrected p < 0.001; HipR: η2 = 0.358, corrected p = 0.012) still held after the Bonferroni correction. Our findings provide evidence for macrostructural plasticity of the contralateral hippocampus in patients with unilateral hippocampal glioma. Specifically, HATA and fimbria exhibit great potential in this process. • Glioma infiltration of the hippocampal regions induces a significant increase in gray matter volume on the contralateral side. • Specifically, the HATA and fimbria regions exhibit favorable plastic potential in the process of lesion-induced structural remolding.

BACKGROUND:The default mode network (DMN) is thought to be involved in the pathophysiology of bipolar depression (BD). However, the findings of prior studies on DMN alterations in BD are inconsistent. Thus, this study aimed to systematically investigate functional abnormalities of the DMN in BD patients. METHODS:We systematically searched PubMed, Ovid, and Web of Science for functional neuroimaging studies on regional homogeneity, amplitude of low frequency fluctuations (ALFF), and functional connectivity of the DMN in BD patients published before March 18, 2022. The stereotactic coordinates of the reported altered brain regions were extracted and incorporated into a brain map using the coordinate-based activation likelihood estimation approach. RESULTS:A total of 43 original research studies were included in the meta-analysis. BD patients showed specific changes in the DMN including decreased ALFF/fractional ALFF in the left cingulate gyrus (CG) and bilateral precuneus (PCUN); increased functional connectivity (FC) in the left CG, left posterior CG, left PCUN, bilateral medial frontal gyrus, and bilateral superior frontal gyrus; and decreased FC in the left CG, left PCUN, left inferior parietal lobule, and left postcentral gyrus. LIMITATIONS:Conclusions are limited by the small number of studies, additional meta-analyses are needed to obtain more data in BD subgroup. CONCLUSION:This meta-analysis supports specific changes in DMN activity and FC in BD patients, which may be powerful biomarkers for the diagnosis of BD. The CG and PCUN were the most affected regions and are thus potential targets for clinical interventions to delay BD progression.

Functional magnetic resonance imaging (fMRI) is a convolution of latent neural activity and the hemodynamic response function (HRF). According to prior studies, the neurodegenerative process in idiopathic Parkinson's Disease (PD) interacts significantly with neuromuscular abnormalities. Although these underlying neuromuscular changes might influence the temporal characteristics of HRF and fMRI signals, relatively few studies have explored this possibility. We hypothesized that such alterations would engender changes in estimated functional connectivity (FC) in fMRI space compared to latent neural space. To test these theories, we calculated voxel-level HRFs by deconvolving resting-state fMRI data from PD patients (n = 61) and healthy controls (HC) (n = 47). Significant group differences in HRF (P < 0.05, Gaussian random field-corrected) were observed in several regions previously associated with PD. Subsequently, we focused on putamen-seed-based FC differences between the PD and HC groups using fMRI and latent neural signals. The results suggested that neglecting HRF variability may cultivate false-positive and false-negative FC group differences. Furthermore, HRF was related to dopamine receptor type 2 (DRD2) gene expression (P < 0.001, t = -7.06, false discover ratecorrected). Taken together, these findings reveal HRF variation and its possible underlying molecular mechanism in PD, and suggest that deconvolution could reduce the impact of HRF variation on FC group differences. (c) 2023 IBRO. Published by Elsevier Ltd. All rights reserved.