The Reduced Gray Matter Volume and Functional Connectivity of the Cerebellum in Type 2 Diabetes Mellitus with High Insulin Resistance.

INTRODUCTION: Insulin resistance is widely thought to be a critical feature in type 2 diabetes mellitus (T2DM), and there is significant evidence indicating a higher abundance of insulin receptors in the human cerebellum than cerebrum. However, the specific structural or functional changes in the cerebellum related to T2DM remain unclear, and the association between cerebellar alterations, insulin resistance, cognition, and emotion is yet to be determined. METHODS: We investigated neuropsychological performance, and structural and functional changes in specific cerebellar subregions in 43 T2DM patients with high insulin resistance (T2DM-highIR), 72 T2DM patients with low insulin resistance (T2DM-lowIR), and 50 controls. Furthermore, the correlation and stepwise multiple linear regression analysis were performed. RESULTS: Compared to the controls, T2DM exhibited lower cognitive scores and higher depressive/anxious scores. Furthermore, T2DM-highIR patients showed reduced gray matter volume (GMV) in the right cerebellar lobules VIIb, Crus I/II, and T2DM showed reduced GMV in left lobules I-IV compared to controls. Additionally, functional connectivity decrease was observed between the right lobules I-V and orbital part of the superior frontal gyrus in T2DM-highIR compared to both T2DM-lowIR and controls. Notably, there were negative correlations between the GMV of the lobules VIIb, Crus I/II, and updated homeostatic model assessment of insulin resistance, and positive correlation with executive/visuospatial performance in T2DM patients. CONCLUSIONS: These results suggest that the cerebellar lobules VIIb, Crus I/II, represent vulnerable brain regions in the context of insulin resistance. Overall, this study offers new insights into the neuropathophysiological mechanisms of brain impairment in patients with T2DM.

Frequency-Specific Alterations of Fractional Amplitude of Low-Frequency Fluctuations in Adult-Onset Hypothyroidism.

BACKGROUND: The neuropathophysiological mechanisms of brain damage underlying hypothyroidism remain unclear. Fractional amplitude of low-frequency fluctuations (fALFF) has been established as a reliable indicator for investigation of abnormal spontaneous brain activity that occurs at specific frequencies in different types of mental disorder. However, the changes of fALFF in specific frequency bands in hypothyroidism have not yet been investigated. METHODS: Fifty-three hypothyroid patients and 39 healthy controls (HCs) underwent thyroid-related hormone levels tests, neuropsychological assessment, and magnetic resonance imaging (MRI) scans. The fALFF in the standard band (0.01-0.1 Hz), slow-4 (0.027-0.073 Hz), and slow-5 bands (0.01-0.027 Hz) were analyzed. An analysis of Pearson correlation was conducted between fALFF, thyroid-related hormone levels, and neuropsychological scores in hypothyroid patients. RESULTS: Compared to HCs, within the routine band, hypothyroidism group showed significantly decreased fALFF in left lingual gyrus, middle temporal gyrus (MTG), precentral gyrus, calcarine cortex, and right inferior occipital gyrus; within the slow-5 band, the hypothyroidism group exhibited decreased fALFF in left lingual gyrus, MTG, superior temporal gyrus, postcentral gyrus, and paracentral lobule, and increased fALFF in supplementary motor area (SMA) and right middle frontal gyrus; additionally, fALFF in the left lingual gyrus within the routine and slow-5 bands were negatively correlated with the level of thyroid stimulating hormone. CONCLUSIONS: In this study, the slow-5 frequency band exhibits better sensitivity than the standard band in detecting fALFF values. A decrease of fALFF values in the lingual gyrus and MTG was observed in both the standard and slow-5 bands and might present potential neuroimaging biomarkers for hypothyroidism. CLINICAL TRIAL REGISTRATION: No: ChiCTR2000028966. Registered 9 January, 2020, https://www.chictr.org.cn.

Alterations of Spontaneous Cortical and Subcortical Activity in Type 2 Diabetes Mellitus with and without Depression.

INTRODUCTION: Type 2 diabetes mellitus (T2DM) patients with depression have a higher risk of complications and mortality than T2DM without depression. However, the exact neuropathophysiological mechanism remains unclear. Consequently, the current study aimed to investigate the alteration of cortical and subcortical spontaneous neural activity in T2DM patients with and without depression. METHODS: The demographic data, clinical variables, neuropsychological tests, and functional and anatomical magnetic resonance imaging of depressed T2DM (n = 47) of non-depressed T2DM (n = 59) and healthy controls (n = 41) were collected and evaluated. The correlation analysis, stepwise multiple linear regression, and receiver operating characteristic curve were performed for further analysis. RESULTS: Abnormal neural activities in the bilateral posterior cingulate cortex (PCC) and hippocampus were observed in depressed and non-depressed T2DM and the right putamen of the depressed T2DM. Interestingly, the subcortical degree centrality (DC) of the right hippocampus and putamen were higher in depressed than non-depressed T2DM. Furthermore, the cortical amplitude of low-frequency fluctuation (ALFF) in PCC, subcortical DC in the putamen of depressed T2DM, and hippocampus of non-depressed T2DM was correlated with cognitive scores. In contrast, the cortical fractional ALFF in PCC of non-depressed T2DM was correlated with depression scores. CONCLUSIONS: The abnormalities of spontaneous cortical activity in PCC and subcortical activity in the hippocampus might represent the neurobiological feature of cerebral dysfunction in T2DM. Notably, the altered subcortical activity in the right putamen might mainly associate with negative emotion in T2DM, which could be a promising biomarker for recognizing early cerebral dysfunction in depressed T2DM. This study provided a novel insight into the neuropathophysiological mechanism of brain dysfunction in T2DM with and without depression.

Segmental Abnormalities of White Matter Microstructure in Primary Hypothyroidism Identified by Automated Fiber Quantification.

INTRODUCTION: Hypothyroidism leads to impaired white matter (WM) integrity, associated with cognitive/neuropsychiatric dysfunction. However, the specific segmental abnormalities of the fibers remain unexplored. Therefore, this study aimed to investigate whether the damage of the WM is limited to a specific segment or the entire bundle via diffusion metrics using automated fiber quantification. METHODS: A cross-sectional study was conducted on 31 hypothyroid patients and 28 healthy controls. Thyroid-related hormone levels, cognitive/neuropsychiatric function, and diffusion tensor image data were collected and analyzed. Correlation and random forest analyses were also performed. RESULTS: The mean fractional anisotropy (FA) values were reduced at the fiber tract level. The mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) values were increased in several fiber tracts, i.e., cingulum cingulate (CC), anterior forceps of corpus callosum (CCF_A). Significant correlations were found between cognitive function and diffusion indicators such as the FA value of the left corticospinal tract and arcuate fasciculus (AF), the MD value of left CC, the RD value of left AF, the AD value of left CC, and CCF_A. The widespread microstructure disruption was spread on multiple specific segments of different tracts at the point-wise level. The random forest revealed that the accuracy of recognizing hypothyroid patients was 82.5%, with the anterior component of CCF_A having the most significant contribution. CONCLUSION: WM microstructural integrity impairments were found in multi-segments of the multiple fiber bundles in hypothyroidism, which might be a potential mechanism of the underlying neurocognitive decline and cerebral impairment. The CCF_A might serve as a neuro biomarker for early warning of cerebral impairment in hypothyroidism.

Correlation between Intrinsic Brain Activity and Thyroid-Stimulating Hormone Level in Unmedicated Bipolar II Depression.

BACKGROUND/AIMS: Although abnormalities of amplitude of low-frequency fluctuations (ALFF) and hormone levels of hypothalamus-pituitary-thyroid axis have been reported in patients with bipolar disorder (BD), the association between abnormal ALFF and serum thyroid hormone levels remains unknown. METHOD: A total of 90 patients with unmedicated BD II depression and 100 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging, and then routine band (0.01-0.1 Hz), slow-5 band (0.01-0.027 Hz), and slow-4 band (0.027-0.073 Hz) ALFF analysis were performed. Additionally, serum thyroid hormone levels including free tri-iodothyronine (FT3), total tri-iodothyronine (TT3), free thyroxin (FT4), total thyroxin (TT4), and thyroid-stimulating hormone (TSH) were detected. The correlation between abnormal serum thyroid hormone levels and ALFF values in patients with BD II depression was calculated. RESULTS: Compared with the HCs, patients with BD II depression showed decreased ALFF in bilateral precuneus (PCu)/posterior cingulate cortex (PCC) in routine and slow-4 frequency bands, decreased ALFF in the right PCu, and increased ALFF in the right middle occipital gyrus (MOG) in the slow-5 frequency band. Additionally, patients with BD II depression showed lower TSH level than HCs, and TSH level was positively correlated with ALFF values in the bilateral PCu/PCC in the routine frequency band. CONCLUSIONS: These findings suggest that patients with BD II depression display intrinsic activity abnormalities, mainly in the PCu/PCC and MOG, which are associated with specific frequency bands. Moreover, altered intrinsic activity in the PCu/PCC may be related to TSH levels in bipolar II depression.

Altered cerebellar functional connectivity in remitted bipolar disorder: A resting-state functional magnetic resonance imaging study.

OBJECTIVES: Several recent studies have reported a strong association between the cerebellar structural and functional abnormalities and psychiatric disorders. However, there are no studies to investigate possible changes in cerebellar functional connectivity in bipolar disorder. This study aimed to examine the whole-brain functional connectivity pattern of patients with remitted bipolar disorder II, in particular in the cerebellum. METHODS: A total of 25 patients with remitted bipolar disorder II and 25 controls underwent resting-state functional magnetic resonance imaging and neuropsychological tests. Voxel-wise whole-brain connectivity was analyzed using a graph theory approach: functional connectivity strength. A seed-based resting-state functional connectivity analysis was further performed to investigate abnormal functional connectivity pattern of those regions with changed functional connectivity strength. RESULTS: Remitted bipolar disorder II patients had significantly decreased functional connectivity strength in the bilateral posterior lobes of cerebellum (mainly lobules VIIb/VIIIa). The seed-based functional connectivity analyses revealed decreased functional connectivity between the right posterior cerebellum and the default mode network (i.e. right posterior cingulate cortex/precuneus and right superior temporal gyrus), bilateral hippocampus, right putamen, left paracentral lobule and bilateral posterior cerebellum and decreased functional connectivity between the left posterior cerebellum and the right inferior parietal lobule and bilateral posterior cerebellum in patients with remitted bipolar disorder II. CONCLUSION: Our results suggest that cerebellar dysconnectivity, in particular distributed cerebellar-cerebral functional connectivity, might be associated with the pathogenesis of bipolar disorder.

Serum brain-derived neurotrophic factor levels in type 2 diabetes mellitus patients and its association with cognitive impairment: A meta-analysis.

OBJECTIVE: To compare the serum levels of brain-derived neurotrophic factor (BDNF) in type 2 diabetes mellitus (T2DM) patients with healthy controls (HC) and evaluate the BDNF levels in T2DM patients with/without cognitive impairment. METHODS: PubMed, EMBASE, and the Cochrane Library databases were searched for the published English literature on BDNF in T2DM patients from inception to December 2022. The BDNF data in the T2DM and HC groups were extracted, and the study quality was evaluated using the Agency for Healthcare Research and Quality. A meta-analysis of the pooled data was conducted using Review Manager 5.3 and Stata 12.0 software. RESULTS: A total of 18 English articles fulfilled with inclusion criteria. The standard mean difference of the serum BDNF level was significantly lower in T2DM than that in the HC group (SMD: -2.04, z = 11.19, P <0.001). Besides, T2DM cognitive impairment group had a slightly lower serum BDNF level compared to the non-cognitive impairment group (SMD: -2.59, z = 1.87, P = 0.06). CONCLUSION: BDNF might be involved in the neuropathophysiology of cerebral damage in T2DM, especially cognitive impairment in T2DM.

Structural and Functional Alterations of Hippocampal Subfields in Patients With Adult-Onset Primary Hypothyroidism.

CONTEXT: Hypothyroidism is often associated with cognitive and emotional dysregulation; however, the underlying neuropathological mechanisms remain elusive. OBJECTIVE: The study aimed to characterize abnormal alterations in hippocampal subfield volumes and functional connectivity (FC) in patients with subclinical hypothyroidism (SCH) and overt hypothyroidism (OH). METHODS: This cross-sectional observational study comprised 47 and 40 patients with newly diagnosed adult-onset primary SCH and OH, respectively, and 53 well-matched healthy controls (HCs). The demographics, clinical variables, and neuropsychological scale scores were collected. Next, the hippocampal subfield volumes and seed-based FC were compared between the groups. Finally, correlation analyses were performed. RESULTS: SCH and OH exhibited significant alterations in cognitive and emotional scale scores. Specifically, the volumes of the right granule cell molecular layer of the dentate gyrus (GC-ML-DG) head, cornu ammonis (CA) 4, and CA3 head were reduced in the SCH and OH groups. Moreover, the volumes of the right molecular layer head, CA1 body, left GC-ML-DG head, and CA4 head were lower in SCH. In addition, the hippocampal subfield volumes decreased more significantly in SCH than OH. The seed-based FC decreased in SCH but increased in OH compared with HCs. Correlation analyses revealed thyroid hormone was negatively correlated with FC values in hypothyroidism. CONCLUSION: Patients with SCH and OH might be at risk of cognitive decline, anxiety, or depression, and exhibited alterations in volume and FC in specific hippocampal subfields. Furthermore, the reduction in volume was more pronounced in SCH. This study provides novel insights into the neuropathological mechanisms of brain impairment in hypothyroidism.

Decreased structural pathways mediating functional connectivity in obstructive sleep apnea.

BACKGROUND: Obstructive sleep apnea (OSA) is a common sleep breathing disorder that is often accompanied by changes in structural connectivity (SC) and functional connectivity (FC). However, the current understanding of the interaction between SC and FC in OSA is still limited. METHODS: The aim of this study is to integrate complementary neuroimaging modalities into a unified framework using multi-layer network analysis methods and to reveal their complex interrelationships. We introduce a new graph metric called SC-FC bandwidth, which measures the throughput of SC mediating FC in a multi-layer network. The bandwidth differences between two groups are evaluated using the network-based statistics (NBS) method. Additionally, we traced and analyzed the SC pathways corresponding to the abnormal bandwidth. RESULTS: In both the healthy control and patients with OSA, the majority offunctionally synchronized nodes were connected via SC paths of length 2. With the NBS method, we observed significantly lower bandwidth between the right Posterior cingulate gyrus and right Cuneus, bilateral Middle frontal gyrus, bilateral Gyrus rectus in OSA patients. By tracing the high-proportion SC pathways, it was found that OSA patients typically exhibit a decrease in direct SC-FC, SC-FC triangles, and SC-FC quads intra- and inter-networks. CONCLUSION: Complex interrelationship changes have been observed between the SC and FC in patients with OSA, which might leads to abnormal information transmission and communication in the brain network.

Alterations in corpus callosum subregions morphology and functional connectivity in patients with adult-onset hypothyroidism.

BACKGROUND: Magnetic resonance imaging (MRI) brain abnormalities have been reported in the corpus callosum (CC) of patients with adult-onset hypothyroidism. However, no study has directly compared CC-specific morphological or functional alterations among subclinical hypothyroidism (SCH), overt hypothyroidism (OH), and healthy controls (HC). Moreover, the association of CC alterations with cognition and emotion is not well understood. METHODS: Demographic data, clinical variables, neuropsychological scores, and MRI data of 152 participants (60 SCH, 37 OH, and 55 HC) were collected. This study investigated the clinical performance, morphological and functional changes of CC subregions across three groups. Moreover, a correlation analysis was performed to explore potential relationships between these factors. RESULTS: Compared to HC, SCH and OH groups exhibited lower cognitive scores and higher depressive/anxious scores. Notably, rostrum and rostral body volume of CC was larger in the SCH group. Functional connectivity between rostral body, anterior midbody and the right precentral and dorsolateral superior frontal gyrus were increased in the SCH group. In contrast, the SCH and OH groups exhibited a decline in functional connectivity between splenium and the right angular gyrus. Within the SCH group, rostrum volume demonstrated a negative correlation with Montreal Cognitive Assessment and visuospatial/executive scores, while displaying a positive correlation with 24-item Hamilton Depression Rating Scale scores. In the OH group, rostral body volume exhibited a negative correlation with serum thyroid stimulating hormone levels, while a positive correlation with serum total thyroxine and free thyroxine levels. CONCLUSIONS: This study suggests that patients with different stages of adult-onset hypothyroidism may exhibit different patterns of CC abnormalities. These findings offer new insights into the neuropathophysiological mechanisms in hypothyroidism.

Prediction of prostate cancer aggressiveness using magnetic resonance imaging radiomics: a dual-center study.

PURPOSE: The Gleason score (GS) and positive needles are crucial aggressive indicators of prostate cancer (PCa). This study aimed to investigate the usefulness of magnetic resonance imaging (MRI) radiomics models in predicting GS and positive needles of systematic biopsy in PCa. MATERIAL AND METHODS: A total of 218 patients with pathologically proven PCa were retrospectively recruited from 2 centers. Small-field-of-view high-resolution T2-weighted imaging and post-contrast delayed sequences were selected to extract radiomics features. Then, analysis of variance and recursive feature elimination were applied to remove redundant features. Radiomics models for predicting GS and positive needles were constructed based on MRI and various classifiers, including support vector machine, linear discriminant analysis, logistic regression (LR), and LR using the least absolute shrinkage and selection operator. The models were evaluated with the area under the curve (AUC) of the receiver-operating characteristic. RESULTS: The 11 features were chosen as the primary feature subset for the GS prediction, whereas the 5 features were chosen for positive needle prediction. LR was chosen as classifier to construct the radiomics models. For GS prediction, the AUC of the radiomics models was 0.811, 0.814, and 0.717 in the training, internal validation, and external validation sets, respectively. For positive needle prediction, the AUC was 0.806, 0.811, and 0.791 in the training, internal validation, and external validation sets, respectively. CONCLUSIONS: MRI radiomics models are suitable for predicting GS and positive needles of systematic biopsy in PCa. The models can be used to identify aggressive PCa using a noninvasive, repeatable, and accurate diagnostic method.

Multimodal data integration for predicting progression risk in castration-resistant prostate cancer using deep learning: a multicenter retrospective study.

Purpose: Patients with advanced prostate cancer (PCa) often develop castration-resistant PCa (CRPC) with poor prognosis. Prognostic information obtained from multiparametric magnetic resonance imaging (mpMRI) and histopathology specimens can be effectively utilized through artificial intelligence (AI) techniques. The objective of this study is to construct an AI-based CRPC progress prediction model by integrating multimodal data. Methods and materials: Data from 399 patients diagnosed with PCa at three medical centers between January 2018 and January 2021 were collected retrospectively. We delineated regions of interest (ROIs) from 3 MRI sequences viz, T2WI, DWI, and ADC and utilized a cropping tool to extract the largest section of each ROI. We selected representative pathological hematoxylin and eosin (H&E) slides for deep-learning model training. A joint combined model nomogram was constructed. ROC curves and calibration curves were plotted to assess the predictive performance and goodness of fit of the model. We generated decision curve analysis (DCA) curves and Kaplan-Meier (KM) survival curves to evaluate the clinical net benefit of the model and its association with progression-free survival (PFS). Results: The AUC of the machine learning (ML) model was 0.755. The best deep learning (DL) model for radiomics and pathomics was the ResNet-50 model, with an AUC of 0.768 and 0.752, respectively. The nomogram graph showed that DL model contributed the most, and the AUC for the combined model was 0.86. The calibration curves and DCA indicate that the combined model had a good calibration ability and net clinical benefit. The KM curve indicated that the model integrating multimodal data can guide patient prognosis and management strategies. Conclusion: The integration of multimodal data effectively improves the prediction of risk for the progression of PCa to CRPC.

Current status and quality of radiomic studies for predicting KRAS mutations in colorectal cancer patients: A systematic review and meta­analysis.

PURPOSE: The purpose of this study was to evaluate the methodological quality of radiomics-based studies for noninvasive, preoperative prediction of Kirsten rat sarcoma (KRAS) mutations in patients with colorectal cancer; furthermore, we systematically evaluate the diagnostic accuracy of predicting models. METHODS: We systematically searched PubMed, Embase, Cochrane Library and Web of Science databases up to 20 April 2022 for eligible studies. The methodological quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools. A meta-analysis of studies on the prediction of KRAS status in colorectal cancer patients was performed. RESULT: Twenty-nine studies were identified in the systematic review, including three studies on the prediction of KRAS status in colorectal cancer liver metastases. All studies had an average RQS score of 9.55 (26.5% of the total score), ranging from 3 to 17. Most studies demonstrated a low or unclear risk of bias in the domains of QUADAS-2. Nineteen studies were included in the meta-analysis, mostly imaged with magnetic resonance imaging (MRI), followed by computed tomography (CT), positron emission tomography-CT (PET/CT). With pooled sensitivity, specificity and area under the curve (AUC) of the training cohorts were 0.80(95% confidence interval(CI), 0.75-0.84), 0.80(95% CI, 0.74-0.85) and 0.87(95% CI, 0.84-0.90),respectively. The pooled sensitivity, specificity, and AUC for the validation cohorts (13 studies) were 0.78(95% CI, 0.71-0.84), 0.84(95% CI, 0.74-0.90), and 0.86(95% CI, 0.83-0.89), respectively. CONCLUSION: Radiomics is a potential noninvasive technology that has a moderate preoperative diagnosis and prediction effect on KRAS mutations. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the methodological quality of the study and further externally validate the model using multicenter datasets.

Radiogenomics study to predict the nuclear grade of renal clear cell carcinoma.

Purpose: To develop models based on radiomics and genomics for predicting the histopathologic nuclear grade with localized clear cell renal cell carcinoma (ccRCC) and to assess whether macro-radiomics models can predict the microscopic pathological changes. Method: In this multi-institutional retrospective study, a computerized tomography (CT) radiomic model for nuclear grade prediction was developed. Utilizing a genomics analysis cohort, nuclear grade-associated gene modules were identified, and a gene model was constructed based on top 30 hub mRNA to predict the nuclear grade. Using a radiogenomic development cohort, biological pathways were enriched by hub genes and a radiogenomic map was created. Results: The four-features-based SVM model predicted nuclear grade with an area under the curve (AUC) score of 0.94 in validation sets, while a five-gene-based model predicted nuclear grade with an AUC of 0.73 in the genomics analysis cohort. A total of five gene modules were identified to be associated with the nuclear grade. Radiomic features were only associated with 271 out of 603 genes in five gene modules and eight top 30 hub genes. Differences existed in the enrichment pathway between associated and un-associated with radiomic features, which were associated with two genes of five-gene signatures in the mRNA model. Conclusion: The CT radiomics models exhibited higher predictive performance than mRNA models. The association between radiomic features and mRNA related to nuclear grade is not universal.

Brain morphology and functional connectivity alterations in patients with severe obstructive sleep apnea.

BACKGROUND: It has been demonstrated that widespread structural and functional brain alterations influence the development of cognitive impairment in patients with obstructive sleep apnea (OSA). However, the literature has limited evidence regarding the neuropathophysiological mechanisms behind these impairments. This research aimed to investigate brain morphologic and functional connectivity (FC) abnormalities related to neurocognitive function in OSA. METHODS: Fifty treatment-naïve males, newly diagnosed patients with severe OSA, and 50 well-matched healthy controls (HCs) were enrolled prospectively. All subjects underwent an MRI scan, cognitive psychological and sleep scale assessment. The differences of brain morphological and seed-based FC between the two groups were compared. The correlation analysis and receiver operating characteristic curve were performed for further analysis. RESULTS: Compared with HCs, the right brainstem, left dorsal-lateral superior frontal gyrus (SFGdor), and superior temporal gyrus (STG) exhibited atrophy in the OSA group. In addition, FC between the left SFGdor and the right postcentral gyrus (PoCG) was increased, which was positively correlated with disease duration (r = 0.312, FDR-corrected P = 0.027). The Jacobian values of the brainstem were negatively correlated with MoCA and recall scores (r = -0.449, FDR-corrected P = 0.0025; r = -0.416, FDR-corrected P = 0.005). Furthermore, the Jacobian values of the left SFGdor demonstrated a relatively high diagnostic performance (sensitivity: 86%, specificity: 56%, AUC: 0.740, 95% CI: 0.643-0.836, P < 0.0001). CONCLUSIONS: Structural atrophy in brainstem and frontotemporal lobe and altered FC may be the neurobiological hallmark of brain impairment in OSA. Notably, brainstem atrophy has been associated with cognitive impairment, which may provide new insights into understanding the neuropathophysiological mechanisms of cognitive impairment in OSA patients.

Alterations in Gray Matter Morphology and Functional Connectivity in Adult Patients with Newly Diagnosed Untreated Hypothyroidism.

Background: Untreated adult hypothyroidism may be associated with cognitive and emotional impairment, but the precise underlying neuropathological mechanism is unknown. We investigated the brain morphological and functional abnormalities associated with cognition and emotion in hypothyroidism. Methods: This is a cross-sectional observational study. Forty-four newly diagnosed adult hypothyroid patients and 54 well-matched healthy controls (HCs) were enrolled. All participants underwent three-dimensional T1-weighted imaging and resting-state functional magnetic resonance imaging (MRI). Morphological and seed-based functional connectivity (FC) analyses were performed to compare the intergroup differences. Neuropsychological tests, including the Montreal Cognitive Assessment (MoCA) Scale, 24-item Hamilton Depression Rating Scale (HAMD-24), and Hamilton Anxiety Rating Scale (HAMA) were administered. Thyroid function test and blood lipid levels were measured. Correlations were computed between neuropsychological and biochemical measures with neuroimaging indices. Sensitive morphological or functional neuroimaging indicators were identified using receiver operating characteristic (ROC) analysis. Results: Compared with HCs, hypothyroid patients demonstrated lower total and subdomain scores on the MoCA and higher HAMD-24 and HAMA scores. Morphological analysis revealed the hypothyroid patients had significantly reduced gray matter (GM) volumes in the right superior frontal gyrus, superior temporal gyrus, left dorsolateral superior frontal gyrus, middle frontal gyrus, and supplementary motor area as well as significantly increased GM volumes in the bilateral cerebellar Crus I and left precentral gyrus. Furthermore, seed-based FC analysis of hypothyroid patients showed increased FC between the right cerebellar Crus I and left precentral gyrus, triangular part of the inferior frontal gyrus, and angular gyrus of the inferior parietal lobe. The language scores of the MoCA were positively correlated with Jacobian values of the left supplementary motor area (r = 0.391, p = 0.046) and precentral gyrus (r = 0.401, p = 0.039). ROC analysis revealed FC value between cerebellar Crus I and angular gyrus could differentiate groups with relatively high accuracy (sensitivity: 75%, specificity: 77.8%, area under the curve: 0.794 [CI 0.701-0.888], p < 0.001). Conclusions: Untreated adult-onset hypothyroidism may be associated with impaired cognition and anxiety or depression. GM morphological alterations and FC of the cerebellum with subregions of the frontal and parietal lobes may represent key neuropathological mechanisms underlying the cognitive deterioration and mood dysregulation observed in hypothyroid adults. Clinical Trial Registration Number: chiCTR2000028966.

Potential predictive value of CT radiomics features for treatment response in patients with COVID-19.

INTRODUCTION: This study aims to explore the predictive value of CT radiomics and clinical characteristics for treatment response in COVID-19 patients. METHODS: Data were collected from clinical/auxiliary examinations and follow-ups of COVID-19 patients. Whole lung radiomics feature extraction was performed at baseline chest CT. Radiomics, clinical, and combined features (nomogram) were evaluated for predicting treatment response. RESULTS: Among 36 COVID-19 patients, mild, common, severe, and critical disease symptoms were found in 1, 21, 13, and 1 of them, respectively. Twenty-five (1 mild, 18 common, and 6 severe) patients showed a good response to treatment and 11 poor/fair responses. The clinical classification (p = 0.025) and serum creatinine (p = 0.010) on admission and small area emphasis (p = 0.036) from radiomics analysis significantly differed between the two groups. Predictive models were constructed based on the radiomics, clinical features, and nomogram showing an area under the curve of 0.651, 0.836, and 0.869, respectively. The nomogram achieved good calibration. CONCLUSION: This new, non-invasive, and low-cost prediction model that combines the radiomics and clinical features is useful for identifying COVID-19 patients who may not respond well to treatment.

Altered thalamic functional connectivity and cerebral blood flow in insomnia disorder: a resting-state functional magnetic resonance imaging study.

BACKGROUND AND PURPOSE: The thalamus plays a crucial role in sleep regulation, but few studies have examined functional connectivity of the thalamus in insomnia disorder. This study aimed to investigate the connectivity patterns and perfusion of the thalamus in patients with insomnia disorder using resting-state functional connectivity and three-dimensional arterial spin labeling (3D ASL). MATERIALS AND METHODS: In total, 56 patients with insomnia disorder and 59 healthy control participants with a similar age-, gender-, and education lever distribution underwent resting-state functional magnetic resonance imaging (rs-fMRI) and 3D-ASL. The thalamus was selected as the seed region. Whole-brain connectivity was assessed using rs-fMRI. Cerebral blood flow (CBF) of the bilateral thalamus was measured with 3D-ASL using region-of-interest (ROI) analysis. All participants completed a series of neuropsychological assessments. Sleep parameters were assessed via polysomnography (PSG). The relationships between imaging parameters and clinical variables were assessed with Pearson correlation analysis. RESULTS: Compared with healthy controls, patients with insomnia disorder exhibited increased connectivity between the left thalamus and right precentral gyrus, and right thalamus and left middle frontal gyrus (MFG), right superior parietal lobule (SPL) and right superior frontal gyrus (SFG). Whereas decreased connectivity was noted between the right thalamus and left posterior cerebellar lobe including Crus I, Crus II, and VII b/VII. Connectivity between the right thalamus and left Crus I was positively correlated with MoCA scores (r = 0.286, P = 0.036) in insomnia disorder. CONCLUSIONS: Our findings illustrate functional abnormalities in brain connectivity and their relationship with cognitive impairments in insomnia disorder, providing novel insight into the neural mechanisms of insomnia disorder.

Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis.

Objectives: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models. Methods: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks' test to assess publication bias. Results: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00. Conclusions: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.

Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis.

Purpose: The purpose of this study was to evaluate the diagnostic accuracy of artificial intelligence (AI) models with magnetic resonance imaging(MRI) in predicting pathological complete response(pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer. Furthermore, assessed the methodological quality of the models. Methods: We searched PubMed, Embase, Cochrane Library, and Web of science for studies published before 21 June 2022, without any language restrictions. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools were used to assess the methodological quality of the included studies. We calculated pooled sensitivity and specificity using random-effects models, I2 values were used to measure heterogeneity, and subgroup analyses to explore potential sources of heterogeneity. Results: We selected 21 papers for inclusion in the meta-analysis from 1562 retrieved publications, with a total of 1873 people in the validation groups. The meta-analysis showed that AI models based on MRI predicted pCR to nCRT in patients with rectal cancer: a pooled area under the curve (AUC) 0.91 (95% CI, 0.88-0.93), sensitivity of 0.82(95% CI,0.71-0.90), pooled specificity 0.86(95% CI,0.80-0.91). In the subgroup analysis, the pooled AUC of the deep learning(DL) model was 0.97, the pooled AUC of the radiomics model was 0.85; the pooled AUC of the combined model with clinical factors was 0.92, and the pooled AUC of the radiomics model alone was 0.87. The mean RQS score of the included studies was 10.95, accounting for 30.4% of the total score. Conclusions: Radiomics is a promising noninvasive method with high value in predicting pathological response to nCRT in patients with rectal cancer. DL models have higher predictive accuracy than radiomics models, and combined models incorporating clinical factors have higher diagnostic accuracy than radiomics models alone. In the future, prospective, large-scale, multicenter investigations using radiomics approaches will strengthen the diagnostic power of pCR. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42021285630.