Most recent paper

Resting-state functional nuclear magnetic resonance imaging in patients with bipolar disorder: Beyond euthymia

Fri, 07/01/2022 - 18:00

Rev Colomb Psiquiatr (Engl Ed). 2022 Jun 28:S2530-3120(22)00040-6. doi: 10.1016/j.rcpeng.2022.06.005. Online ahead of print.


INTRODUCTION: Functional nuclear magnetic resonance imaging in the resting state (R-fMRI) allows the identification of complete functional connectivity networks and the possible neuronal correlations of psychiatric disorders. The literature on R-fMRI and bipolar disorder (BD) will be reviewed, emphasising the findings in the phases of mania, hypomania and depression.

METHODS: It is a narrative review of the literature in which articles were searched in PubMed and Embase, with the key words in English "bipolar disorder" AND "resting state", without limit on the date of publication.

RESULTS: The studies of BD patients in the mania and hypomania phases who underwent R-fMRI show concordant results in terms of decreased functional cerebral connectivity between the amygdala and some cortical regions, which indicates that this functional connection would have some implication in the normal affect regulation. Patients in the depressive phase show a decrease in functional brain connectivity, but as there are several anatomical structures involved and neural networks reported in the studies, it is not possible to compare them.

CONCLUSIONS: There is a decrease in functional connectivity in patients with BD, but current evidence does not allow establishing specific changes in specific functional brain connectivity networks. However, there are already some findings that show correlation with the patients' symptoms.

PMID:35778347 | DOI:10.1016/j.rcpeng.2022.06.005

The Relationship between Cerebrovascular Reactivity and Cerebral Oxygenation During Hemodialysis

Fri, 07/01/2022 - 18:00

J Am Soc Nephrol. 2022 Jul 1:ASN.2021101353. doi: 10.1681/ASN.2021101353. Online ahead of print.


BACKGROUND: Patients with kidney failure treated with hemodialysis (HD) may be at risk for cerebral hypoperfusion due to HD-induced BP decline in the setting of impaired cerebral autoregulation. Cerebrovascular reactivity (CVR), the cerebrovascular response to vasoactive stimuli, may be a useful indicator of cerebral autoregulation in the HD population and identify those at risk for cerebral hypoperfusion. We hypothesize that CVR combined with intradialytic BP changes will be associated with declines in cerebral oxygenation saturation (ScO2) during HD.

METHODS: Participants completed the MRI scans on a non-HD day and cerebral oximetry during HD. We measured CVR with resting-state fMRI (rs-fMRI) without a gas challenge and ScO2 saturation with near-infrared spectroscopy. Regression analysis was used to examine the relationship between intradialytic cerebral oxygen desaturation, intradialytic BP, and CVR in different gray matter regions.

RESULTS: Twenty-six patients on HD had complete data for analysis. Sixteen patients were men, 18 had diabetes, and 20 had hypertension. Mean±SD age was 65.3±7.2 years, and mean±SD duration on HD was 11.5±9.4 months. CVR in the anterior cingulate gyrus (ACG; P=0.03, r2 =0.19) and insular cortex (IC; P=0.03, r2 =0.19) regions negatively correlated with decline in intradialytic ScO2. Model prediction of intradialytic ScO2 improved when including intradialytic BP change and ultrafiltration rate to the ACG rsCVR (P<0.01, r2 =0.48) and IC rsCVR (P=0.02, r2 =0.35) models, respectively.

CONCLUSIONS: We found significant relationships between regional rsCVR measured in the brain and decline in intradialytic ScO2. Our results warrant further exploration of using CVR in determining a patient's risk of cerebral ischemic injury during HD.

PMID:35777782 | DOI:10.1681/ASN.2021101353

Accurate predictions of individual differences in task-evoked brain activity from resting-state fMRI using a sparse ensemble learner

Fri, 07/01/2022 - 18:00

Neuroimage. 2022 Jun 28:119418. doi: 10.1016/j.neuroimage.2022.119418. Online ahead of print.


Modelling and predicting individual differences in task-fMRI activity can have a wide range of applications from basic to clinical neuroscience. It has been shown that models based on resting-state activity can have high predictive accuracy. Here we propose several improvements to such models. Using a sparse ensemble learner, we show that (i) features extracted using Stochastic Probabilistic Functional Modes (sPROFUMO) outperform the previously proposed dual-regression approach, (ii) that the shape and overall intensity of individualised task activations can be modelled separately and explicitly, (iii) training the model on predicting residual differences in brain activity further boosts individualised predictions. These results hold for both surface-based analyses of the Human Connectome Project data as well as volumetric analyses of UK-biobank data. Overall, our model achieves state of the art prediction accuracy on par with the test-retest reliability of task-fMRI scans, suggesting that it has potential to supplement traditional task localisers.

PMID:35777635 | DOI:10.1016/j.neuroimage.2022.119418

Transient, developmental functional and structural connectivity abnormalities in the thalamocortical motor network in Rolandic epilepsy

Fri, 07/01/2022 - 18:00

Neuroimage Clin. 2022 Jun 27;35:103102. doi: 10.1016/j.nicl.2022.103102. Online ahead of print.


Rolandic epilepsy (RE) is the most common focal, idiopathic, developmental epilepsy, characterized by a transient period of sleep-potentiated seizures and epileptiform discharges in the inferior Rolandic cortex during childhood. The cause of RE remains unknown but converging evidence has identified abnormalities in the Rolandic thalamocortical circuit. To better localize this transient disease, we evaluated Rolandic thalamocortical functional and structural connectivity in the sensory and motor circuits separately during the symptomatic and asymptomatic phases of this disease. We collected high resolution structural, diffusion, and resting state functional MRI data in a prospective cohort of children with active RE (n = 17), resolved RE (n = 21), and controls (n = 33). We then computed the functional and structural connectivity between the inferior Rolandic cortex and the ventrolateral (VL) nucleus of the thalamus (efferent pathway) and the ventroposterolateral (VPL) nucleus of the thalamus (afferent pathway) across development in children with active, resolved RE and controls. We compared connectivity with age in each group using linear mixed-effects models. We found that children with active RE have increasing thalamocortical functional connectivity between the VL thalamus and inferior motor cortex with age (p = 0.022) that is not observed in controls or resolved RE. In contrast, children with resolved RE have increasing thalamocortical structural connectivity between the VL nucleus and the inferior motor cortex with age (p = 0.025) that is not observed in controls or active RE. No relationships were identified between VPL nuclei and the inferior sensory cortex with age in any group. These findings localize the functional and structural thalamocortical circuit disruption in RE to the efferent thalamocortical motor pathway. Further work is required to determine how these circuit abnormalities contribute to the emergence and resolution of symptoms in this developmental disease.

PMID:35777251 | DOI:10.1016/j.nicl.2022.103102

DeepEZ: A Graph Convolutional Network for Automated Epileptogenic Zone Localization from Resting-State fMRI Connectivity

Fri, 07/01/2022 - 18:00

IEEE Trans Biomed Eng. 2022 Jul 1;PP. doi: 10.1109/TBME.2022.3187942. Online ahead of print.


OBJECTIVE: Epileptogenic zone (EZ) localization is a crucial step during diagnostic work up and therapeutic planning in medication refractory epilepsy. In this paper, we present the first deep learning approach to localize the EZ based on resting-state fMRI (rs-fMRI) data.

METHODS: Our network, called DeepEZ, uses a cascade of graph convolutions that emphasize signal propagation along expected anatomical pathways. We also integrate domain-specific information, such as an asymmetry term on the predicted EZ and a learned subject-specific bias to mitigate environmental confounds.

RESULTS: We validate DeepEZ on rs-fMRI collected from 14 patients with focal epilepsy at the University of Wisconsin Madison. Using cross validation, we demonstrate that DeepEZ achieves consistently high EZ localization performance (Accuracy: 0.88 ± 0.03; AUC: 0.73 ± 0.03) that far outstripped any of the baseline methods. This performance is notable given the variability in EZ locations and scanner type across the cohort.

CONCLUSION: Our results highlight the promise of using DeepEZ as an accurate and noninvasive therapeutic planning tool for medication refractory epilepsy.

SIGNIFICANCE: While prior work in EZ localization focused on identifying localized aberrant signatures, there is growing evidence that epileptic seizures affect inter-regional connectivity in the brain. DeepEZ allows clinicians to harness this information from noninvasive imaging that can easily be integrated into the existing clinical workflow.

PMID:35776823 | DOI:10.1109/TBME.2022.3187942

K-Means Clustering Algorithm-Based Functional Magnetic Resonance for Evaluation of Regular Hemodialysis on Brain Function of Patients with End-Stage Renal Disease

Fri, 07/01/2022 - 18:00

Comput Math Methods Med. 2022 Jun 21;2022:1181030. doi: 10.1155/2022/1181030. eCollection 2022.


This research was to evaluate the effects of regular hemodialysis (HD) on the brain function of patients with end-stage renal disease (ESRD). Resting-state functional magnetic resonance imaging (rs-fMRI) based on improved k-means clustering algorithm (k-means) was proposed to scan the brains of 30 regular dialysis patients with end-stage renal disease (ESRD) (experimental group) and 30 normal volunteers (control group). The proposed algorithm was compared with the traditional k-means algorithm and mean shift algorithm and applied to the magnetic resonance scan of patients with ESRD on long-term regular HD. The results showed that the neuropsychological cognitive function (NSCF) evaluation result of the test group was much better than that of the control group, and the difference was statistically obvious (P < 0.05). The results of blood biochemistry, Digit Symbol Substitution Test (DSST), and Montreal Cognitive Assessment Scale (MoCA) in the test group showed no statistical difference compared with those in the control group. The running time of the improved k-means algorithm was dramatically shorter than that of traditional k-means algorithm, showing statistical difference (P < 0.05). Comparison among the improved and traditional k-means algorithm and mean shift algorithm suggested that the improved k-means algorithm showed a lower error rate for image segmentation, and the differences were statistically remarkable (P < 0.05). In conclusion, the improved k-means algorithm showed better time efficiency and the lowest error rate in processing rs-fMRI images than the traditional k-means algorithm and mean shift algorithm, and the effects of regular HD on the brains of patients with ESRD were evaluated effectively.

PMID:35774296 | PMC:PMC9239818 | DOI:10.1155/2022/1181030

Functional MRI Changes in Patients after Thyroidectomy under General Anesthesia

Fri, 07/01/2022 - 18:00

Biomed Res Int. 2022 Jun 21;2022:1935125. doi: 10.1155/2022/1935125. eCollection 2022.


Cognitive changes affecting elderly patients following surgery under anesthesia have drawn significant attention and have been investigated in considerable depth. Resting-state functional magnetic resonance imaging (rs-fMRI) can be used to assess changes in brain functional connectivity (FC) associated with postoperative changes in cognition, a common complication in seniors undergoing surgery. In this study, we recruited 20 patients over 55 of age and scheduled an elective thyroidectomy under general anesthesia to assess perioperative changes in brain FC density (FCD) in patients undergoing thyroidectomy under general anesthesia using rs-fMRI. All 20 patients underwent a series of clinical, quantitative, neurological, and neuropsychological tests and fMRI examinations on the day before surgery (Day 0) and 7 days after surgery (Day 7). The following tests were conducted on all patients: the Minimental State Examination (MMSE), the digit symbol substitution test (DSST), the trail making test (part A), the verbal fluency test, and Warrington's recognition memory test (WRMT). FMRI data were acquired using a 3T MR system; the FCD values were calculated using the REST software package. We used paired t-tests to compare the FCD between Day 7 and Day 0. A value of p < 0.05 was considered to reflect statistical significance. The postoperative FCD was significantly reduced in the supplementary motor area (SMA). Analyses of the percentage changes of errors in the WRMT revealed a significant and negative correlation with the mean percentage change of FCD in the SMA (Spearman's r = -0.54, 95% CI: (-0.80, -0.12), p = 0.014). Postoperative changes in FCD in the SMA may be associated with the perioperative neurocognitive changes in patients undergoing partial thyroidectomy under general anesthesia.

PMID:35774279 | PMC:PMC9239812 | DOI:10.1155/2022/1935125

Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network

Fri, 07/01/2022 - 18:00

Front Aging Neurosci. 2022 Jun 14;14:866230. doi: 10.3389/fnagi.2022.866230. eCollection 2022.


BACKGROUND: Detection of mild cognitive impairment (MCI) is essential to screen high risk of Alzheimer's disease (AD). However, subtle changes during MCI make it challenging to classify in machine learning. The previous pathological analysis pointed out that the hippocampus is the critical hub for the white matter (WM) network of MCI. Damage to the white matter pathways around the hippocampus is the main cause of memory decline in MCI. Therefore, it is vital to biologically extract features from the WM network driven by hippocampus-related regions to improve classification performance.

METHODS: Our study proposes a method for feature extraction of the whole-brain WM network. First, 42 MCI and 54 normal control (NC) subjects were recruited using diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI), and T1-weighted (T1w) imaging. Second, mean diffusivity (MD) and fractional anisotropy (FA) were calculated from DTI, and the whole-brain WM networks were obtained. Third, regions of interest (ROIs) with significant functional connectivity to the hippocampus were selected for feature extraction, and the hippocampus (HIP)-related WM networks were obtained. Furthermore, the rank sum test with Bonferroni correction was used to retain significantly different connectivity between MCI and NC, and significant HIP-related WM networks were obtained. Finally, the classification performances of these three WM networks were compared to select the optimal feature and classifier.

RESULTS: (1) For the features, the whole-brain WM network, HIP-related WM network, and significant HIP-related WM network are significantly improved in turn. Also, the accuracy of MD networks as features is better than FA. (2) For the classification algorithm, the support vector machine (SVM) classifier with radial basis function, taking the significant HIP-related WM network in MD as a feature, has the optimal classification performance (accuracy = 89.4%, AUC = 0.954). (3) For the pathologic mechanism, the hippocampus and thalamus are crucial hubs of the WM network for MCI.

CONCLUSION: Feature extraction from the WM network driven by hippocampus-related regions provides an effective method for the early diagnosis of AD.

PMID:35774112 | PMC:PMC9237212 | DOI:10.3389/fnagi.2022.866230

Inflammation, Amygdala-Ventromedial Prefrontal Functional Connectivity and Symptoms of Anxiety and PTSD in African American Women Recruited from an Inner-City Hospital: Preliminary Results

Thu, 06/30/2022 - 18:00

Brain Behav Immun. 2022 Jun 27:S0889-1591(22)00165-9. doi: 10.1016/j.bbi.2022.06.013. Online ahead of print.


Inflammatory stimuli have been shown to impact brain regions involved in threat detection and emotional processing including amygdala and ventromedial prefrontal cortex (vmPFC), and to increase anxiety. Biomarkers of endogenous inflammation, including inflammatory cytokines and C-reactive protein (CRP), are reliably elevated in a subset of patients with depression and anxiety-related disorders such as post-traumatic stress disorder (PTSD), and have been associated with high anxiety in population studies. We previously reported that plasma CRP and cytokines in patients with depression were negatively correlated with resting-state functional connectivity (FC) between right amygdala and vmPFC, as assessed using both ROI to voxel-wise and targeted FC approaches, in association with symptoms of anxiety, particularly in patients with comorbid anxiety disorders or PTSD. To determine whether relationships between inflammation, right amygdala-vmPFC FC, and anxiety are reproducible across patient samples and research settings, we employed an a priori, hypothesis-driven approach to examine relationships between inflammation, targeted right amygdala-vmPFC FC and anxiety in a cohort of African American (AA) women (n=54) recruited from an inner-city hospital population reliably found to have higher levels of inflammation (median CRP ∼4 mg/L) as well as symptoms of anxiety, depression and PTSD. Higher concentrations of plasma CRP were associated with lower right amygdala-vmPFC FC (r=-0.32, p=0.017), and this relationship remained significant when controlling for age, body mass index and number of lifetime trauma events experienced, as well as severity of PTSD and depression symptoms (all p<0.05). This amygdala-vmPFC FC was similarly associated with a composite score of three inflammatory cytokines in a subset of women where plasma was available for analysis (n=33, r=-0.33, p=0.058; adjusted r=-0.43, p=0.026 when controlling for covariates including PTSD and depression symptom severity). Lower right amygdala-vmPFC FC was in turn associated with higher levels of anxiety reported to be generally experienced on the State-Trait Anxiety Inventory, trait component (adjusted r=-0.32, p=0.039 when controlling for covariates). Exploratory analyses also revealed a negative correlation between severity of childhood maltreatment and right amygdala-vmPFC FC (r=-0.32, p=0.018) that was independent of CRP and its association with FC, as well as an association between low amygdala-vmPFC FC and severity of PTSD symptoms, specifically the re-experiencing/intrusive symptom subscale (adjusted r=-0.32, p=0.028 when controlling for covariates). While CRP was not linearly associated with either anxiety or PTSD symptoms, CRP concentrations were higher in women reporting clinically significant anxiety or PTSD symptom severity when these symptoms were considered together (both p<0.05), but with no interaction. These results support our primary hypothesis that higher inflammation was associated with lower amygdala-vmPFC FC, a relationship that was detected using a hypothesis-driven, targeted approach. Findings also support that this phenotype of high CRP and low vmPFC FC was observed in association with anxiety in primary analyses, as well as symptoms of PTSD in exploratory analyses, in a cohort recruited from an inner-city population of AA women enriched for high inflammation, history of trauma exposure, and symptom severity. Larger, longitudinal samples are required to fully tease apart causal relationships between inflammatory biomarkers, FC and PTSD-related symptoms in future studies.

PMID:35772683 | DOI:10.1016/j.bbi.2022.06.013

Abnormal functional connectivities patterns of multidomain cognitive impairments in pontine stroke patients

Thu, 06/30/2022 - 18:00

Hum Brain Mapp. 2022 Jun 30. doi: 10.1002/hbm.25982. Online ahead of print.


Cognitive dysfunction in patients with infratentorial stroke has been paid little attention. Brainstem stroke may disrupt network connectivity across the whole brain and affect multidomain cognition, but the details of this process remain unclear. The study aimed to investigate the effects of stroke-induced pontine injury on whole-brain network connectivity and cognitive function. We included 47 patients with pontine stroke and 56 healthy comparisons (HC), who underwent cognitive tests and functional magnetic resonance imaging (fMRI). Seven meaningful brain networks were identified using independent component analysis (ICA). Patients with pontine stroke had decreased intra-network functional connectivities (FCs) in the primary perceptual and higher cognitive control networks, including sensorimotor network (SMN), visual network (VIS), default mode network (DMN), and salience network (SAN), as well as decreased inter-network FCs in the primary perceptual (VIS-SMN) and higher cognitive control networks (bilateral frontoparietal networks, rFPN-lFPN). While the FCs between the primary perceptual and higher cognitive control networks (VIS-DMN, VIS-rFPN, VIS-lFPN) were increased. Furthermore, the alterations in these FCs correlated with patients' cognitive measurements. These findings suggested that the infratentorial stroke can induce dysfunctional connectivity in both primary perceptual and higher cognitive control networks at the whole-brain level, which may be attributable to the neural substrates of multidomain cognitive deficits in these patients.

PMID:35770854 | DOI:10.1002/hbm.25982

Neurobiological Alterations in Females With PTSD: A Systematic Review

Thu, 06/30/2022 - 18:00

Front Psychiatry. 2022 Jun 13;13:862476. doi: 10.3389/fpsyt.2022.862476. eCollection 2022.


Most females experience at least one traumatic event in their lives, but not all develop PTSD. Despite considerable research, our understanding of the key factors that constitute risk for PTSD among females is limited. Previous research has largely focused on sex differences, neglecting within group comparisons, thereby obviating differences between females who do and do not develop PTSD following exposure to trauma. In this systematic review, we conducted a search for the extent of existing research utilizing magnetic resonance imaging (MRI) to examine neurobiological differences among females of all ages, with and without PTSD. Only studies of females who met full diagnostic criteria for PTSD were included. Fifty-six studies were selected and reviewed. We synthesized here findings from structural MRI (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and resting state functional connectivity (rs-FC MRI) studies, comparing females with and without PTSD. A range of biopsychosocial constructs that may leave females vulnerable to PTSD were discussed. First, the ways timing and type of exposure to trauma may impact PTSD risk were discussed. Second, the key role that cognitive and behavioral mechanisms may play in PTSD was described, including rumination, and deficient fear extinction. Third, the role of specific symptom patterns and common comorbidities in female-specific PTSD was described, as well as sex-specific implications on treatment and parenting outcomes. We concluded by identifying areas for future research, to address the need to better understand developmental aspects of brain alterations, the differential impact of trauma types and timing, the putative role of neuroendocrine system in neurobiology of PTSD among females, and the impact of social and cultural factors on neurobiology in females with PTSD.

PMID:35770056 | PMC:PMC9234306 | DOI:10.3389/fpsyt.2022.862476

Alterations of Cerebral Perfusion and Functional Connectivity in Children With Idiopathic Generalized Epilepsy

Thu, 06/30/2022 - 18:00

Front Neurosci. 2022 Jun 13;16:918513. doi: 10.3389/fnins.2022.918513. eCollection 2022.


BACKGROUND: Studies have demonstrated that adults with idiopathic generalized epilepsy (IGE) have functional abnormalities; however, the neuropathological pathogenesis differs between adults and children. This study aimed to explore alterations in the cerebral blood flow (CBF) and functional connectivity (FC) to comprehensively elucidate the neuropathological mechanisms of IGE in children.

METHODS: We obtained arterial spin labeling (ASL) and resting state functional magnetic resonance imaging data of 28 children with IGE and 35 matched controls. We used ASL to determine differential CBF regions in children with IGE. A seed-based whole-brain FC analysis was performed for regions with significant CBF changes. The mean CBF and FC of brain areas with significant group differences was extracted, then its correlation with clinical variables in IGE group was analyzed by using Pearson correlation analysis.

RESULTS: Compared to controls, children with IGE had CBF abnormalities that were mainly observed in the right middle temporal gyrus, right middle occipital gyrus (MOG), right superior frontal gyrus (SFG), left inferior frontal gyrus (IFG), and triangular part of the left IFG (IFGtriang). We observed that the FC between the left IFGtriang and calcarine fissure (CAL) and that between the right MOG and bilateral CAL were decreased in children with IGE. The CBF in the right SFG was correlated with the age at IGE onset. FC in the left IFGtriang and left CAL was correlated with the IGE duration.

CONCLUSION: This study found that CBF and FC were altered simultaneously in the left IFGtriang and right MOG of children with IGE. The combination of CBF and FC may provide additional information and insight regarding the pathophysiology of IGE from neuronal and vascular integration perspectives.

PMID:35769697 | PMC:PMC9236200 | DOI:10.3389/fnins.2022.918513

Is genetic risk of ADHD mediated via dopaminergic mechanism? A study of functional connectivity in ADHD and pharmacologically challenged healthy volunteers with a genetic risk profile

Wed, 06/29/2022 - 18:00

Transl Psychiatry. 2022 Jun 29;12(1):264. doi: 10.1038/s41398-022-02003-y.


Recent GWAS allow us to calculate polygenic risk scores for ADHD. At the imaging level, resting-state fMRI analyses have given us valuable insights into changes in connectivity patterns in ADHD patients. However, no study has yet attempted to combine these two different levels of investigation. For this endeavor, we used a dopaminergic challenge fMRI study (L-DOPA) in healthy participants who were genotyped for their ADHD, MDD, schizophrenia, and body height polygenic risk score (PRS) and compared results with a study comparing ADHD patients and healthy controls. Our objective was to evaluate how L-DOPA-induced changes of reward-system-related FC are dependent on the individual polygenic risk score. FMRI imaging was used to evaluate resting-state functional connectivity (FC) of targeted subcortical structures in 27 ADHD patients and matched controls. In a second study, we evaluated the effect of ADHD and non-ADHD PRS in a L-DOPA-based pharmaco-fMRI-challenge in 34 healthy volunteers. The functional connectivity between the putamen and parietal lobe was decreased in ADHD patients. In healthy volunteers, the FC between putamen and parietal lobe was lower in ADHD high genetic risk participants. This direction of connectivity was reversed during L-DOPA challenge. Further findings are described for other dopaminergic subcortical structures. The FC between the putamen and the attention network showed the most consistent change in patients as well as in high-risk participants. Our results suggest that FC of the dorsal attention network is altered in adult ADHD as well as in healthy controls with higher genetic risk.

PMID:35768414 | DOI:10.1038/s41398-022-02003-y

Altered effective connectivity among core brain networks in patients with bipolar disorder

Wed, 06/29/2022 - 18:00

J Psychiatr Res. 2022 Jun 15;152:296-304. doi: 10.1016/j.jpsychires.2022.06.031. Online ahead of print.


BACKGROUND: Bipolar disorder (BD) is increasingly being regarded as a dysconnection syndrome. Functional integration among the three core brain networks - executive control network (ECN), salience network (SN), and default mode network (DMN) - is abnormal in patients with BD; however, the causal relationship among the three networks in BD is largely unknown. It is also unclear whether patients with BD in different mood states show distinct effective connectivity patterns during rest.

METHODS: Resting-state fMRI data were collected from 65 patients with BD and 85 healthy controls. Spectral dynamic causal modeling was applied to investigate the effective connectivity difference of the three brain networks between all patients with BD and healthy controls and between patients who were in euthymic mood state (euthymic BD) and depressed mood state (depressed BD).

RESULTS: Compared with healthy controls, all patients with BD showed altered effective connectivity within and between the ECN and SN and from these two networks to the DMN. Compared with patients with depressed BD, patients with euthymic BD showed increased excitatory effects within the ECN and decreased inhibitory effects from the SN to the ECN and DMN.

CONCLUSION: These results further confirmed that patients with BD show abnormal functional integration within and among the three core brain networks, and exhibit similar and different effective connectivity patterns in different mood states. Abnormal effective connectivity has the potential to be a critical index for diagnosing BD and differentiating between BD patients with different mood states.

PMID:35767917 | DOI:10.1016/j.jpsychires.2022.06.031

Multi-Center and Multi-Channel Pooling GCN for Early AD Diagnosis Based on Dual-Modality Fused Brain Network

Wed, 06/29/2022 - 18:00

IEEE Trans Med Imaging. 2022 Jun 29;PP. doi: 10.1109/TMI.2022.3187141. Online ahead of print.


For significant memory concern (SMC) and mild cognitive impairment (MCI), their classification performance is limited by confounding features, diverse imaging protocols, and limited sample size. To address the above limitations, we introduce a dual-modality fused brain connectivity network combining resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), and propose three mechanisms in the current graph convolutional network (GCN) to improve classifier performance. First, we introduce a DTI-strength penalty term for constructing functional connectivity networks. Stronger structural connectivity and bigger structural strength diversity between groups provide a higher opportunity for retaining connectivity information. Second, a multi-center attention graph with each node representing a subject is proposed to consider the influence of data source, gender, acquisition equipment, and disease status of those training samples in GCN. The attention mechanism captures their different impacts on edge weights. Third, we propose a multi-channel mechanism to improve filter performance, assigning different filters to features based on feature statistics. Applying those nodes with low-quality features to perform convolution would also deteriorate filter performance. Therefore, we further propose a pooling mechanism, which introduces the disease status information of those training samples to evaluate the quality of nodes. Finally, we obtain the final classification results by inputting the multi-center attention graph into the multi-channel pooling GCN. The proposed method is tested on three datasets (i.e., an ADNI 2 dataset, an ADNI 3 dataset, and an in-house dataset). Experimental results indicate that the proposed method is effective and superior to other related algorithms, with a mean classification accuracy of 93.05% in our binary classification tasks. Our code is available at:

PMID:35767511 | DOI:10.1109/TMI.2022.3187141

Pain avoidance and functional connectivity between insula and amygdala identifies suicidal attempters in patients with major depressive disorder using machine learning

Wed, 06/29/2022 - 18:00

Psychophysiology. 2022 Jun 29:e14136. doi: 10.1111/psyp.14136. Online ahead of print.


Pain avoidance can effectively classify suicide attempters from non-attempters among patients with major depressive disorder (MDD). However, the neural circuits underlying pain processing in suicide attempters have not been described comprehensively. In Study 1, we recruited MDD patients with a history of suicide attempts (MDD-SA), and those without (MDD-NSA) to examine the patterns of psychological pain using the latent profile analysis. Further, in Study 2, participants including the MDD-SA, MDD-NSA, and healthy controls underwent resting-state functional magnetic resonance imaging. We used machine learning that included features of gray matter volume (GMV), the functional connectivity (FC) brain patterns of the region of interest, and behavioral data to identify suicide attempters. The results identified three latent classes of psychological pain in MDD patients: the low pain class (18.9%), the painful feeling class (37.2%), and the pain avoidance class (43.9%). Furthermore, the proportion of suicide attempters with high pain avoidance was the highest. The accuracy of multimodality classifiers (63%-92%) was significantly higher than that of brain-only classifiers (56%-85%) and behavior-only classifiers (64%-73%). Pain avoidance ranked first in the optimal feature set of the suicide attempt classification model. The crucial brain imaging features were FC between the left amygdala and right insula, right orbitofrontal and left thalamus, left anterior cingulate cortex and left insula, right orbitofrontal, amygdala, and the GMV of right thalamus. Additionally, the optimal feature set, including pain avoidance and crucial brain patterns of psychological pain neural circuits, was provided for the identification of suicide attempters.

PMID:35767231 | DOI:10.1111/psyp.14136

Static and dynamic topological organization of brain functional connectome in acute mild traumatic brain injury

Wed, 06/29/2022 - 18:00

Acta Radiol. 2022 Jun 28:2841851221109897. doi: 10.1177/02841851221109897. Online ahead of print.


BACKGROUND: Prior studies have detected topological changes of brain functional networks in patients with acute mild traumatic brain injury (mTBI). However, the alterations of dynamic topological characteristics in mTBI have been scarcely elucidated.

PURPOSE: To evaluate static and dynamic functional connectivity topological networks in patients with acute mTBI using resting-state functional magnetic resonance imaging (fMRI).

MATERIAL AND METHODS: A total of 55 patients with acute mTBI and 55 age-, sex-, and education-matched healthy controls (HCs) were enrolled in this study. All participants underwent resting-state fMRI scans, and data were analyzed using graph-theory methods and a sliding window approach. Post-traumatic cognitive performance and resting-state fMRI data were collected within one week after injury. Static and dynamic functional connectivity patterns were determined by independent component analysis. Spearman's correlation analysis was further performed between fMRI changes and Montreal cognitive assessment (MoCA) scores.

RESULTS: Global efficiency was lower (P = 0.02), and local efficiency (P < 0.001) and mean Cp (P < 0.001) were higher in patients with acute mTBI than in HCs. Local efficiency was correlated with visuospatial/executive performance (r = -0.421; P = 0.002) in patients with acute mTBI. Significant differences in nodal efficiency and node degree centrality (P < 0.01) were found between the mTBI and HC groups. For dynamic properties, patients with mTBI showed higher variance (P = 0.016) in global efficiency than HCs.

CONCLUSIONS: The present study shows that patients with mTBI have abnormal brain functional connectome topology, especially the dynamic graph theory characteristics, which provide new insights into the role of topological network properties in patients with acute mTBI.

PMID:35765198 | DOI:10.1177/02841851221109897

Altered brain regional homogeneity is associated with depressive symptoms in COVID-19

Tue, 06/28/2022 - 18:00

J Affect Disord. 2022 Jun 25:S0165-0327(22)00722-4. doi: 10.1016/j.jad.2022.06.061. Online ahead of print.


BACKGROUND: COVID-19 is an infectious disease that has spread worldwide in 2020, causing a severe pandemic. In addition to respiratory symptoms, neuropsychiatric manifestations are commonly observed, including chronic fatigue, depression, and anxiety. The neural correlates of neuropsychiatric symptoms in COVID-19 are still largely unknown.

METHODS: A total of 79 patients with COVID-19 (COV) and 17 healthy controls (HC) underwent 3 T functional magnetic resonance imaging at rest as well as structural imaging. Regional homogeneity (ReHo) was calculated. We also measured anxiety using the General Anxiety Disorder 7-item scale, depressive symptoms with the Patient Health Questionnaire (PHQ-9), and fatigue with the Multidimension Fatigue Inventory, respectively.

RESULTS: In comparison with HC, COV showed significantly higher depressive scores. Moreover, COV presented reduced ReHo in the left angular gyrus, the right superior/middle temporal gyrus and the left inferior temporal gyrus, and higher ReHo in the right hippocampus. No differences in gray matter were detected in these areas. Furthermore, we observed a negative correlation between ReHo in the left angular gyrus and PHQ-9 score and a trend toward a positive correlation between ReHo in the right hippocampus and the PHQ-9 scores.

LIMITATIONS: Heterogeneity in the clinical presentation in COV, the different timing from the first positive molecular swab test to the MRI, and the cross-sectional design of the study limit the generalization of our findings.

CONCLUSIONS: Our results suggest that COVID-19 infection may contribute to depressive symptoms via a modulation of local functional connectivity in cortico-limbic circuits.

PMID:35764231 | DOI:10.1016/j.jad.2022.06.061

Resting-state network organisation in children with traumatic brain injury

Tue, 06/28/2022 - 18:00

Cortex. 2022 Jun 4;154:89-104. doi: 10.1016/j.cortex.2022.05.014. Online ahead of print.


Children with traumatic brain injury are at risk of neurocognitive and behavioural impairment. Although there is evidence for abnormal brain activity in resting-state networks after TBI, the role of resting-state network organisation in paediatric TBI outcome remains poorly understood. This study is the first to investigate the impact of paediatric TBI on resting-state network organisation using graph theory, and its relevance for functional outcome. Participants were 8-14 years and included children with (i) mild TBI and risk factors for complicated TBI (mildRF+, n = 20), (ii) moderate/severe TBI (n = 15), and (iii) trauma control injuries (n = 27). Children underwent resting-state functional magnetic resonance imaging (fMRI), neurocognitive testing, and behavioural assessment at 2.8 years post-injury. Graph theory was applied to fMRI timeseries to evaluate the impact of TBI on global and local organisation of the resting-state network, and relevance for neurocognitive and behavioural functioning. Children with TBI showed atypical global network organisation as compared to the trauma control group, reflected by lower modularity (mildRF + TBI and moderate/severe TBI), higher smallworldness (mildRF + TBI) and lower assortativity (moderate/severe TBI ps < .04, Cohen's ds: > .6). Regarding local network organisation, the relative importance of hub regions in the network did not differ between groups. Regression analyses showed relationships between global as well as local network parameters with neurocognitive functioning (i.e., working memory, memory encoding; R2 = 23.3 - 38.5%) and behavioural functioning (i.e., externalising problems, R2 = 36.1%). Findings indicate the impact of TBI on global functional network organisation, and the relevance of both global and local network organisation for long-term neurocognitive and behavioural outcome after paediatric TBI. The results suggest potential prognostic value of resting-state network organisation for outcome after paediatric TBI.

PMID:35763900 | DOI:10.1016/j.cortex.2022.05.014

The Effects of Respiratory Muscle Training on Resting-State Brain Activity and Thoracic Mobility in Healthy Subjects: A Randomized Controlled Trial

Tue, 06/28/2022 - 18:00

J Magn Reson Imaging. 2022 Jun 28. doi: 10.1002/jmri.28322. Online ahead of print.


BACKGROUND: Although inspiratory muscle training (IMT) is an effective intervention for improving breath perception, brain mechanisms have not been studied yet.

PURPOSE: To examine the effects of IMT on insula and default mode network (DMN) using resting-state functional MRI (RS-fMRI).

STUDY TYPE: Prospective.

POPULATION: A total of 26 healthy participants were randomly assigned to two groups as IMT group (n = 14) and sham IMT groups (n = 12).

FIELD STRENGTH/SEQUENCE: A 3-T, three-dimensional T2* gradient-echo echo planar imaging sequence for RS-fMRI was obtained.

ASSESSMENT: The intervention group received IMT at 60% and sham group received at 15% of maximal inspiratory pressure (MIP) for 8 weeks. Pulmonary and respiratory muscle function, and breathing patterns were measured. Groups underwent RS-fMRI before and after the treatment.

STATISTICAL TESTS: Statistical tests were two-tailed P < 0.05 was considered statistically significant. Student's t test was used to compare the groups. One-sample t-test for each group was used to reveal pattern of functional connectivity. A statistical threshold of P < 0.001 uncorrected value was set at voxel level. We used False discovery rate (FDR)-corrected P < 0.05 cluster level.

RESULTS: The IMT group showed more prominent alterations in insula and DMN connectivity than sham group. The MIP was significantly different after IMT. Respiratory rate (P = 0.344), inspiratory time (P = 0.222), expiratory time (P = 1.000), and inspiratory time/total breath time (P = 0.572) of respiratory patterns showed no significant change after IMT. All DMN components showed decreased, while insula showed increased activation significantly.

DATA CONCLUSION: Differences in brain activity and connectivity may reflect improved ventilatory perception with IMT with a possible role in regulating breathing pattern by processing interoceptive signals.


PMID:35762913 | DOI:10.1002/jmri.28322