OBJECTIVE: Schizophrenia (SCZ) and psychotic bipolar disorder (PBD) are two major psychotic disorders with similar symptoms and tight associations on the psychopathological level, posing a clinical challenge for their differentiation. This study aimed to investigate and compare the structural connectivity patterns of the limbic system between SCZ and PBD, and to identify specific regional disruptions associated with psychiatric symptoms.
METHODS: Using sMRI data from 146 SCZ, 160 PBD, and 145 healthy control (HC) participants, we employed a data-driven approach to segment the hippocampus, thalamus, hypothalamus, amygdala, and cingulate cortex into subregions. We then investigated the structural connectivity patterns between these subregions at the global and nodal levels. Additionally, we assessed psychotic symptoms by utilizing the subscales of the Brief Psychiatric Rating Scale (BPRS) to examine correlations between symptom severity and network metrics between groups.
RESULTS: Patients with SCZ and PBD had decreased global efficiency (Eglob) (SCZ: adjusted P<0.001; PBD: adjusted P = 0.003), local efficiency (Eloc) (SCZ and PBD: adjusted P<0.001), and clustering coefficient (Cp) (SCZ and PBD: adjusted P<0.001), and increased path length (Lp) (SCZ: adjusted P<0.001; PBD: adjusted P = 0.004) compared to HC. Patients with SCZ showed more pronounced decreases in Eglob (adjusted P<0.001), Eloc (adjusted P<0.001), and Cp (adjusted P = 0.029), and increased Lp (adjusted P = 0.024) compared to patients with PBD. The most notable structural disruptions were observed in the hippocampus and thalamus, which correlated with different psychotic symptoms, respectively.
CONCLUSIONS: This study provides evidence of distinct structural connectivity disruptions in the limbic system of patients with SCZ and PBD. These findings might contribute to our understanding of the neuropathological basis for distinguishing SCZ and PBD.

The pathogenesis of Alzheimer\'s disease (AD) remains unclear, but revealing individual differences in functional connectivity (FC) may provide insights and improve diagnostic precision. A hierarchical clustering-based autoencoder with functional connectivity was proposed to categorize 82 AD patients from the Alzheimer\'s Disease Neuroimaging Initiative. Compared to directly performing clustering, using an autoencoder to reduce the dimensionality of the matrix can effectively eliminate noise and redundant information in the data, extract key features, and optimize clustering performance. Subsequently, subtype differences in clinical and graph theoretical metrics were assessed. Results indicate a significant inter-subject heterogeneity in the degree of FC disruption among AD patients. We have identified two neurophysiological subtypes: subtype I exhibits widespread functional impairment across the entire brain, while subtype II shows mild impairment in the Limbic System region. What is worth noting is that we also observed significant differences between subtypes in terms of neurocognitive assessment scores associations with network functionality, and graph theory metrics. Our method can accurately identify different functional disruptions in subtypes of AD, facilitating personalized treatment and early diagnosis, ultimately improving patient outcomes.

Mild traumatic brain injury (mTBI) is a known risk factor for neurodegenerative diseases, yet the precise pathophysiological mechanisms remain poorly understand, often obscured by group-level analysis in non-invasive neuroimaging studies. Individual-based method is critical to exploring heterogeneity in mTBI. We recruited 80 mTBI patients and 40 matched healthy controls, obtaining high-resolution structural MRI for constructing Individual Differential Structural Covariance Networks (IDSCN). Comparisons were conducted at both the individual and group levels. Connectome-based Predictive Modeling (CPM) was applied to predict cognitive performance based on whole-brain connectivity. During the acute stage of mTBI, patients exhibited significant heterogeneity in the count and direction of altered edges, obscured by group-level analysis. In the chronic stage, the number of altered edges decreased and became more consistent, aligning with clinical observations of acute cognitive impairment and gradual improvement. Subgroup analysis based on loss of consciousness/post-traumatic amnesia revealed distinct patterns of alterations. The temporal lobe, particularly regions related to the limbic system, significantly predicted cognitive function from acute to chronic stage. The use of IDSCN and CPM has provided valuable individual-level insights, reconciling discrepancies from previous studies. Additionally, the limbic system may be an appropriate target for future intervention efforts.

Attention deficit hyperactivity disorder (ADHD) is a common childhood developmental disorder. In recent years, pattern recognition methods have been increasingly applied to neuroimaging studies of ADHD. However, these methods often suffer from limited accuracy and interpretability, impeding their contribution to the identification of ADHD-related biomarkers. To address these limitations, we applied the amplitude of low-frequency fluctuation (ALFF) results for the limbic system and cerebellar network as input data and conducted a binary hypothesis testing framework for ADHD biomarker detection. Our study on the ADHD-200 dataset at multiple sites resulted in an average classification accuracy of 93%, indicating strong discriminative power of the input brain regions between the ADHD and control groups. Moreover, our approach identified critical brain regions, including the thalamus, hippocampal gyrus, and cerebellum Crus 2, as biomarkers. Overall, this investigation uncovered potential ADHD biomarkers in the limbic system and cerebellar network through the use of ALFF realizing highly credible results, which can provide new insights for ADHD diagnosis and treatment.

OBJECTIVE: The study aimed to identify if clinical features and survival outcomes of insular glioma patients are associated with our classification based on the tumor spread.
METHODS: Our study included 283 consecutive patients diagnosed with histological grade 2 and 3 insular gliomas. A new classification was proposed, and tumors restricted to the paralimbic system were defined as type 1. When tumors invaded the limbic system (referred to as the hippocampus and its surrounding structures in this study) simultaneously, they were defined as type 2. Tumors with additional internal capsule involvement were defined as type 3.
RESULTS: Tumors defined as type 3 had a higher age at diagnosis (p = 0.002) and a higher preoperative volume (p < 0.001). Furthermore, type 3 was more likely to be diagnosed as IDH wild type (p < 0.001), with a higher rate of Ki-67 index (p = 0.015) and a lower rate of gross total resection (p < 0.001). Type 1 had a slower tumor growth rate than type 2 (mean 3.3%/month vs. 19.8%/month; p < 0.001). Multivariate Cox regression analysis revealed the extent of resection (HR 0.259, p = 0.004), IDH status (HR 3.694, p = 0.012), and tumor spread type (HR = 1.874, p = 0.012) as independent predictors of overall survival (OS). Tumor grade (HR 2.609, p = 0.008), the extent of resection (HR 0.488, p = 0.038), IDH status (HR 2.225, p = 0.025), and tumor spread type (HR 1.531, p = 0.038) were significant in predicting progression-free survival (PFS).
CONCLUSIONS: The current study proposes a classification of the insular glioma according to the tumor spread. It indicates that the tumors defined as type 1 have a relatively better nature and biological characteristics, and those defined as type 3 can be more aggressive and refractory. Besides its predictive value for prognosis, the classification has potential value in formulating surgical strategies for patients with insular gliomas.

BACKGROUND: Previous investigations have revealed substantial differences in neuroimaging characteristics between healthy controls (HCs) and individuals diagnosed with schizophrenia (SCZ). However, we are not entirely sure how brain activity links to symptoms in schizophrenia, and there is a need for reliable brain imaging markers for treatment prediction.
METHODS: In this longitudinal study, we examined 56 individuals diagnosed with 56 SCZ and 51 HCs. The SCZ patients underwent a three-month course of antipsychotic treatment. We employed resting-state functional magnetic resonance imaging (fMRI) along with fractional Amplitude of Low Frequency Fluctuations (fALFF) and support vector regression (SVR) methods for data acquisition and subsequent analysis.
RESULTS: In this study, we initially noted lower fALFF values in the right postcentral/precentral gyrus and left postcentral gyrus, coupled with higher fALFF values in the left hippocampus and right putamen in SCZ patients compared to the HCs at baseline. However, when comparing fALFF values in brain regions with abnormal baseline fALFF values for SCZ patients who completed the follow-up, no significant differences in fALFF values were observed after 3 months of treatment compared to baseline data. The fALFF values in the right postcentral/precentral gyrus and left postcentral gyrus, and the left postcentral gyrus were useful in predicting treatment effects.
CONCLUSIONS: Our findings suggest that reduced fALFF values in the sensory-motor networks and increased fALFF values in the limbic system may constitute distinctive neurobiological features in SCZ patients. These findings may serve as potential neuroimaging markers for the prognosis of SCZ patients.

Non-invasive brain stimulations have drawn attention in remediating memory decline in older adults. However, it remains unclear regarding the cognitive and neural mechanisms underpinning the neurostimulation effects on memory rehabilitation. We evaluated the intervention effects of 2-weeks of neurostimulations (high-definition transcranial direct current stimulation, HD-tDCS, and electroacupuncture, EA versus controls, CN) on brain activities and functional connectivity during a working memory task in normally cognitive older adults (age 60+, n = 60). Results showed that HD-tDCS and EA significantly improved the cognitive performance, potentiated the brain activities of overlapping neural substrates (i.e. hippocampus, dlPFC, and lingual gyrus) associated with explicit and implicit memory, and modulated the nodal topological properties and brain modular interactions manifesting as increased intramodular connection of the limbic-system dominated network, decreased intramodular connection of default-mode-like network, as well as stronger intermodular connection between frontal-dominated network and limbic-system-dominated network. Predictive model further identified the neuro-behavioral association between modular connections and working memory. This preliminary study provides evidence that noninvasive neurostimulations can improve older adults\' working memory through potentiating the brain activity of working memory-related areas and mediating the modular interactions of related brain networks. These findings have important implication for remediating older adults\' working memory and cognitive declines.

BACKGROUND: Systemic lupus erythematosus (SLE) is an immune-mediated and multi-systemic disease which may affect the nervous system, causing neuropsychiatric SLE (NPSLE). Recent neuroimaging studies have examined brain functional alterations in SLE. However, discrepant findings were reported. This meta-analysis aims to identify consistent resting-state functional abnormalities in SLE.
METHODS: PubMed and Web of Science were searched to identify candidate resting-state functional MRI studies assessing SLE. A voxel-based meta-analysis was performed using the anisotropic effect-size version of the seed-based d mapping (AES-SDM). The abnormal intrinsic functional patterns extracted from SDM were mapped onto the brain functional network atlas to determine brain abnormalities at a network level.
RESULTS: Twelve studies evaluating fifteen datasets were included in this meta-analysis, comprising 572 SLE patients and 436 healthy controls (HCs). Compared with HCs, SLE patients showed increased brain activity in the bilateral hippocampus and right superior temporal gyrus, and decreased brain activity in the left superior frontal gyrus, left middle temporal gyrus, bilateral thalamus, left inferior frontal gyrus and right cerebellum. Mapping the abnormal patterns to the network atlas revealed the default mode network and the limbic system as core neural systems commonly affected in SLE.
CONCLUSIONS: The number of included studies is relatively small, with heterogeneous analytic methods and a risk of publication bias.
CONCLUSIONS: Brain functional alterations in SLE are predominantly found in the default mode network and the limbic system. These findings uncovered a consistent pattern of resting-state functional network abnormalities in SLE which may serve as a potential objective neuroimaging biomarker.

We aimed to elucidate the neurobiological basis of depression in Parkinson\'s disease and identify potential imaging markers for depression in patients with Parkinson\'s disease. We recruited 43 normal controls (NC), 46 depressed Parkinson\'s disease patients (DPD) and 56 non-depressed Parkinson\'s disease (NDPD). All participants underwent routine T2-weighted, T2Flair, and resting-state scans on the same 3.0 T magnetic resonance imaging (MRI) scanner at our hospital. Pre-processing includes calculating surface-based Regional Homogeneity (2DReHo) and cortical thickness. Then we defined the correlation coefficient between 2DReHo and cortical thickness as the functional-structural coupling index. Between-group comparisons were conducted on the Fisher\'s Z-transformed correlation coefficients. To identify specific regions of decoupling, the 2DReHo for each participant were divided by cortical thickness at each vertex, followed by threshold-free cluster enhancement (TFCE) multiple comparison correction. Binary logistic regression analysis was performed with DPD as the dependent variable, and significantly altered indicators as the independent variables. Receiver operating characteristic curves were constructed to compare the diagnostic performance of individual predictors and combinations using R and MedCalc software. DPD patients exhibited a significantly lower whole-brain functional-structural coupling index than NDPD patients and NC. Abnormal functional-structural coupling was primarily observed in the left inferior parietal lobule and right primary and early visual cortices in DPD patients. Receiver operating characteristic analysis revealed that the combination of cortical functional-structural coupling, surface-based ReHo, and thickness had the best diagnostic performance, achieving a sensitivity of 65% and specificity of 77.7%. This is the first study to explore the relationship between functional and structural changes in DPD patients and evaluate the diagnostic performance of these altered correlations to predict depression in Parkinson\'s disease patients. We posit that these changes in functional-structural relationships may serve as imaging biomarkers for depression in Parkinson\'s disease patients, potentially aiding in the classification and diagnosis of Parkinson\'s disease. Additionally, our findings provide functional and structural imaging evidence for exploring the neurobiological basis of depression in Parkinson\'s disease.

OBJECTIVE: Personality, emotions, and olfaction exhibit partial anatomical overlap in the limbic system structure, establishing potential mechanisms between personality, affective disorders, and olfactory-related aspects. Thus, this study aims to investigate the associations among the Big Five personality traits, alexithymia, anxiety symptoms, and odor awareness.
METHODS: A total of 863 college participants were recruited for this study. All participants completed the Chinese Big Five Personality Inventory-15, the Odor Awareness Scale (OAS), the Toronto Alexithymia Scale-20, and the Generalized Anxiety Disorder Screener-7. Structural equation modeling was employed to examine the hypothesized mediated model.
RESULTS: The findings revealed the majority of significant intercorrelations among the dimensions of the Big Five personality traits, alexithymia, anxiety symptoms, and OAS (|r| = 0.072-0.567, p < 0.05). Alexithymia and anxiety symptoms exhibited a serial mediation effect between neuroticism and OAS (95%CI[0.001, 0.014]), conscientiousness and OAS (95%CI[-0.008, -0.001]), and extraversion and OAS (95%CI[-0.006, -0.001]). Anxiety symptoms mediated the relationship between agreeableness and OAS (95%CI[-0.023, -0.001]) and between openness and OAS (95%CI [0.004, 0.024]).
CONCLUSIONS: The mediating roles of alexithymia and anxiety symptoms between the Big Five personality traits and odor awareness support the idea of a certain level of association among personality, emotions, and olfaction, with the underlying role of the limbic system structure. This enhances our understanding of personality, emotions, and olfaction and provides insights for future intervention measures for affective disorders and olfactory dysfunctions.