Most recent paper

Changes in cerebral connectivity and brain tissue pulsations with the antidepressant response to an equimolar mixture of oxygen and nitrous oxide: an MRI and ultrasound study

Thu, 08/17/2023 - 18:00

Mol Psychiatry. 2023 Aug 17. doi: 10.1038/s41380-023-02217-6. Online ahead of print.

ABSTRACT

Nitrous oxide (N2O) has recently emerged as a potential fast-acting antidepressant but the cerebral mechanisms involved in this effect remain speculative. We hypothesized that the antidepressant response to an Equimolar Mixture of Oxygen and Nitrous Oxide (EMONO) would be associated with changes in cerebral connectivity and brain tissue pulsations (BTP). Thirty participants (20 with a major depressive episode resistant to at least one antidepressant and 10 healthy controls-HC, aged 25-50, only females) were exposed to a 1-h single session of EMONO and followed for 1 week. We defined response as a reduction of at least 50% in the MADRS score 1 week after exposure. Cerebral connectivity of the Anterior Cingulate Cortex (ACC), using ROI-based resting state fMRI, and BTP, using ultrasound Tissue Pulsatility Imaging, were compared before and rapidly after exposure (as well as during exposure for BTP) among HC, non-responders and responders. We conducted analyses to compare group × time, group, and time effects. Nine (45%) depressed participants were considered responders and eleven (55%) non-responders. In responders, we observed a significant reduction in the connectivity of the subgenual ACC with the precuneus. Connectivity of the supracallosal ACC with the mid-cingulate also significantly decreased after exposure in HC and in non-responders. BTP significantly increased in the three groups between baseline and gas exposure, but the increase in BTP within the first 10 min was only significant in responders. We found that a single session of EMONO can rapidly modify the functional connectivity in the subgenual ACC-precuneus, nodes within the default mode network, in depressed participants responders to EMONO. In addition, larger increases in BTP, associated with a significant rise in cerebral blood flow, appear to promote the antidepressant response, possibly by facilitating optimal drug delivery to the brain. Our study identified potential cerebral mechanisms related to the antidepressant response of N2O, as well as potential markers for treatment response with this fast-acting antidepressant.

PMID:37592013 | DOI:10.1038/s41380-023-02217-6

Group Surrogate Data Generating Models and Similarity Quantification of Multivariate Time-Series: A Resting-State fMRI Study

Thu, 08/17/2023 - 18:00

Neuroimage. 2023 Aug 15:120329. doi: 10.1016/j.neuroimage.2023.120329. Online ahead of print.

ABSTRACT

Advancements in non-invasive brain analysis through novel approaches such as big data analytics and in silico simulation are essential for explaining brain function and associated pathologies. In this study, we extend the vector auto-regressive surrogate technique from a single multivariate time-series to group data using a novel Group Surrogate Data Generating Model (GSDGM). This methodology allowed us to generate biologically plausible human brain dynamics representative of a large human resting-state (rs-fMRI) dataset obtained from the Human Connectome Project. Simultaneously, we defined a novel similarity measure, termed the Multivariate Time-series Ensemble Similarity Score (MTESS). MTESS showed high accuracy and f-measure in subject identification, and it can directly compare the similarity between two multivariate time-series. We used MTESS to analyze both human and marmoset rs-fMRI data. Our results showed similarity differences between cortical and subcortical regions. We also conducted MTESS and state transition analysis between single and group surrogate techniques, and confirmed that a group surrogate approach can generate plausible group centroid multivariate time-series. Finally, we used GSDGM and MTESS for the fingerprint analysis of human rs-fMRI data, successfully distinguishing normal and outlier sessions. These new techniques will be useful for clinical applications and in silico simulation.

PMID:37591477 | DOI:10.1016/j.neuroimage.2023.120329

The sex differences in anhedonia in major depressive disorder: A resting-state fMRI study

Thu, 08/17/2023 - 18:00

J Affect Disord. 2023 Aug 15:S0165-0327(23)01055-8. doi: 10.1016/j.jad.2023.08.083. Online ahead of print.

ABSTRACT

OBJECTIVE: The external behavioural manifestations and internal neural mechanisms of anhedonia are sexually dimorphic. This study aimed to explore the sex differences in the regional brain neuroimaging features of anhedonia in the context of major depressive disorder (MDD).

METHOD: The resting-fMRI by applying amplitude of low-frequency fluctuation (ALFF) method was estimated in 414 patients with MDD (281 high anhedonia [HA], 133 low anhedonia [LA]) and 213 healthy controls (HC). The effects of two factors in patients with MDD were analysed using a 2 (sex: male, female) × 2 (group: HA, LA) ANOVA concerning the brain regions in which statistical differences were identified between patients with MDD and HC. We followed up with patients with HA at baseline, and 43 patients completed a second fMRI scan in remission. Paired t-test was performed to compare the ALFF values of anhedonia-related brain regions between the baseline and remission periods.

RESULTS: For the sex-by-group interaction, the bilateral insula, right hippocampus, right post cingulum cortex, and left putamen showed significant differences. Furthermore, the abnormally elevated ALFF values in anhedonia-related brain regions at baseline decreased in remission.

CONCLUSION: Our findings point to the fact that the females showed unique patterns of anhedonia-related brain activity compared to males, which may have clinical implications for interfering with the anhedonia symptoms in MDD. Using task fMRI, we can further examine the distinct characteristics between consumption anhedonia and anticipation anhedonia in MDD.

PMID:37591350 | DOI:10.1016/j.jad.2023.08.083

Alterations of PAC-based resting state networks in Parkinson's disease are partially alleviated by levodopa medication

Thu, 08/17/2023 - 18:00

Front Syst Neurosci. 2023 Aug 1;17:1219334. doi: 10.3389/fnsys.2023.1219334. eCollection 2023.

ABSTRACT

INTRODUCTION: Parkinson's disease (PD) is a neurodegenerative disorder affecting the whole brain, leading to several motor and non-motor symptoms. In the past, it has been shown that PD alters resting state networks (RSN) in the brain. These networks are usually derived from fMRI BOLD signals. This study investigated RSN changes in PD patients based on maximum phase-amplitude coupling (PAC) throughout the cortex. We also tested the hypothesis that levodopa medication shifts network activity back toward a healthy state.

METHODS: We recorded 23 PD patients and 24 healthy age-matched participants for 30 min at rest with magnetoencephalography (MEG). PD patients were measured once in the dopaminergic medication ON and once in the medication OFF state. A T1-MRI brain scan was acquired from each participant for source reconstruction. After correcting the data for artifacts and performing source reconstruction using a linearly constrained minimum variance beamformer, we extracted visual, sensorimotor (SMN), and frontal RSNs based on PAC.

RESULTS: We found significant changes in all networks between healthy participants and PD patients in the medication OFF state. Levodopa had a significant effect on the SMN but not on the other networks. There was no significant change in the optimal PAC coupling frequencies between healthy participants and PD patients.

DISCUSSION: Our results suggest that RSNs, based on PAC in different parts of the cortex, are altered in PD patients. Furthermore, levodopa significantly affects the SMN, reflecting the clinical alleviation of motor symptoms and leading to a network normalization compared to healthy controls.

PMID:37588811 | PMC:PMC10427244 | DOI:10.3389/fnsys.2023.1219334

Brain Microstructure and Brain Function Changes in Space Headache by Head-Down-Tilted Bed Rest

Thu, 08/17/2023 - 18:00

Aerosp Med Hum Perform. 2023 Sep 1;94(9):678-685. doi: 10.3357/AMHP.6177.2023.

ABSTRACT

INTRODUCTION: Several astronauts have experienced severe headaches during spaceflight, but no studies have examined the associated brain microstructure and functional changes. Head-down-tilted bed rest (HDBR) is a well-established method for studying the physical effects of microgravity on the ground. In this study, we analyzed the changes in brain microstructure and function during headache caused by HDBR using diffusion tensor imaging (DTI) and resting state functional magnetic resonance imaging (R-fMRI).METHODS: We imaged 28 healthy subjects with DTI and R-fMRI in the horizontal supine position and HDBR. Using Tract-Based Spatial Statistics, fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity were compared between the headache and non-headache groups. Additionally, an analysis of functional connectivity (FC) was performed, followed by a correlation analysis between FC and numerical rating scale.RESULTS: HDBR caused headaches in 21 of 28 subjects. DTI analysis showed no significant change in fractional anisotropy after HDBR, whereas axial diffusivity, radial diffusivity, and mean diffusivity increased significantly. R-fMRI analysis showed a significant decrease in FC in several areas after HDBR. The headache group showed significantly higher FC before HDBR, and both groups showed higher FC after HDBR. Correlation analysis showed a positive correlation between FC and numerical rating scale before HDBR but negative after HDBR.DISCUSSION: We demonstrated the image change in the acute phase of space headache by HDBR using DTI and R-fMRI. Changes in brain microstructure and function specific to patients developing headaches may be evaluated by imaging.Goto M, Shibata Y, Ishiyama S, Matsumaru Y, Ishikawa E. Brain microstructure and brain function changes in space headache by head-down-tilted bed rest. Aerosp Med Hum Perform. 2023; 94(9):678-685.

PMID:37587626 | DOI:10.3357/AMHP.6177.2023

Suicidal thoughts and behaviours among military veterans: protocol for a prospective, observational, neuroimaging study

Wed, 08/16/2023 - 18:00

BMJ Open. 2023 Aug 16;13(8):e070654. doi: 10.1136/bmjopen-2022-070654.

ABSTRACT

INTRODUCTION: This study's overarching goal is to examine the relationship between brain circuits and suicidal thoughts and behaviours (STBs) in a transdiagnostic sample of US military veterans. Because STBs have been linked with maladaptive decision-making and disorders linked to impulsivity, this investigation focuses on valence and inhibitory control circuits.

METHODS AND ANALYSIS: In this prospective, observational study, we will collect functional MRI (fMRI), cognitive and clinical data from 136 veterans (target sample size) recruited from the Providence VA Health System (PVAHS): 68 with STBs and 68 matched controls. Behavioural data will be collected using standardised measures of STBs, psychiatric symptoms, cognition, functioning and medical history. Neuroimaging data will include structural, task and resting fMRI. We will conduct follow-up interviews and assessments at 6, 12 and 24 months post-enrolment. Primary analyses will compare data from veterans with and without STBs and will also evaluate whether activation and connectivity within circuits of valence and inhibition covary with historical and prospective patterns of suicidal ideation and behaviour.

ETHICS AND DISSEMINATION: The PVAHS Institutional Review Board approved this study (2018-051). Written informed consent will be obtained from all participants. Findings from this study will be published in peer-reviewed journals and presented at local, regional, national and international conferences.Nauder Namaky, Ph.D.* nauder_namaky@brown.edu.

PMID:37586858 | DOI:10.1136/bmjopen-2022-070654

Longitudinal developmental trajectories of functional connectivity reveal regional distribution of distinct age effects in infancy

Wed, 08/16/2023 - 18:00

Cereb Cortex. 2023 Aug 16:bhad288. doi: 10.1093/cercor/bhad288. Online ahead of print.

ABSTRACT

Prior work has shown that different functional brain networks exhibit different maturation rates, but little is known about whether and how different brain areas may differ in the exact shape of longitudinal functional connectivity growth trajectories during infancy. We used resting-state functional magnetic resonance imaging (fMRI) during natural sleep to characterize developmental trajectories of different regions using a longitudinal cohort of infants at 3 weeks (neonate), 1 year, and 2 years of age (n = 90; all with usable data at three time points). A novel whole brain heatmap analysis was performed with four mixed-effect models to determine the best fit of age-related changes for each functional connection: (i) growth effects: positive-linear-age, (ii) emergent effects: positive-log-age, (iii) pruning effects: negative-quadratic-age, and (iv) transient effects: positive-quadratic-age. Our results revealed that emergent (logarithmic) effects dominated developmental trajectory patterns, but significant pruning and transient effects were also observed, particularly in connections centered on inferior frontal and anterior cingulate areas that support social learning and conflict monitoring. Overall, unique global distribution patterns were observed for each growth model indicating that developmental trajectories for different connections are heterogeneous. All models showed significant effects concentrated in association areas, highlighting the dominance of higher-order social/cognitive development during the first 2 years of life.

PMID:37585708 | DOI:10.1093/cercor/bhad288

Reproducibility and Sensitivity of Resting-State fMRI in Patients With Parkinson's Disease Using Cross Validation-Based Data Censoring

Wed, 08/16/2023 - 18:00

J Magn Reson Imaging. 2023 Aug 16. doi: 10.1002/jmri.28958. Online ahead of print.

ABSTRACT

BACKGROUND: Uncontrollable body movements are typical symptoms of Parkinson's disease (PD), which results in inconsistent findings regarding resting-state functional connectivity (rsFC) networks, especially for group difference clusters. Systematically identifying the motion-associated data was highly demanded.

PURPOSE: To determine data censoring criteria using a quantitative cross validation-based data censoring (CVDC) method and to improve the detection of rsFC deficits in PD.

STUDY TYPE: Prospective.

SUBJECTS: Forty-one PD patients (68.63 ± 9.17 years, 44% female) and 20 healthy controls (66.83 ± 12.94 years, 55% female).

FIELD STRENGTH/SEQUENCE: 3-T, T1-weighted gradient echo and EPI sequences.

ASSESSMENT: Clusters with significant differences between groups were found in three visual networks, default network, and right sensorimotor network. Five-fold cross-validation tests were performed using multiple motion exclusion criteria, and the selected criteria were determined based on cluster sizes, significance values, and Dice coefficients among the cross-validation tests. As a reference method, whole brain rsFC comparisons between groups were analyzed using a FMRIB Software Library (FSL) pipeline with default settings.

STATISTICAL TESTS: Group difference clusters were calculated using nonparametric permutation statistics of FSL-randomize. The family-wise error was corrected. Demographic information was evaluated using independent sample t-tests and Pearson's Chi-squared tests. The level of statistical significance was set at P < 0.05.

RESULTS: With the FSL processing pipeline, the mean Dice coefficient of the network clusters was 0.411, indicating a low reproducibility. With the proposed CVDC method, motion exclusion criteria were determined as frame-wise displacement >0.55 mm. Group-difference clusters showed a mean P-value of 0.01 and a 72% higher mean Dice coefficient compared to the FSL pipeline. Furthermore, the CVDC method was capable of detecting subtle rsFC deficits in the medial sensorimotor network and auditory network that were unobservable using the conventional pipeline.

DATA CONCLUSION: The CVDC method may provide superior sensitivity and improved reproducibility for detecting rsFC deficits in PD.

LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

PMID:37584329 | DOI:10.1002/jmri.28958

Childhood trauma is linked to abnormal static-dynamic brain topology in adolescents with major depressive disorder

Wed, 08/16/2023 - 18:00

Int J Clin Health Psychol. 2023 Oct-Dec;23(4):100401. doi: 10.1016/j.ijchp.2023.100401. Epub 2023 Aug 5.

ABSTRACT

Childhood trauma is a leading risk factor for adolescents developing major depressive disorder (MDD); however, the underlying neuroimaging mechanisms remain unclear. This study aimed to investigate the association among childhood trauma, MDD and brain dysfunctions by combining static and dynamic brain network models. We recruited 46 first-episode drug-naïve adolescent MDD patients with childhood trauma (MDD-CT), 53 MDD patients without childhood trauma (MDD-nCT), and 90 healthy controls (HCs) for resting-state functional magnetic resonance imaging (fMRI) scans; all participants were aged 13-18 years. Compared to the HCs and MDD-nCT groups, the MDD-CT group exhibited significantly higher global and local efficiency in static brain networks and significantly higher temporal correlation coefficients in dynamic brain network models at the whole-brain level, and altered the local efficiency of default mode network (DMN) and temporal correlation coefficients of DMN, salience (SAN), and attention (ATN) networks at the local perspective. Correlation analysis indicated that altered brain network features and clinical symptoms, childhood trauma, and particularly emotional neglect were highly correlated in adolescents with MDD. This study may provide new evidence for the dysconnectivity hypothesis regarding the associations between childhood trauma and MDD in adolescents from the perspectives of both static and dynamic brain topology.

PMID:37584055 | PMC:PMC10423886 | DOI:10.1016/j.ijchp.2023.100401

Impulsivity-related right superior frontal gyrus as a biomarker of internet gaming disorder

Wed, 08/16/2023 - 18:00

Gen Psychiatr. 2023 Aug 10;36(4):e100985. doi: 10.1136/gpsych-2022-100985. eCollection 2023.

ABSTRACT

BACKGROUND: Internet gaming disorder (IGD) is a mental health issue that affects individuals worldwide. However, the lack of knowledge about the biomarkers associated with the development of IGD has restricted the diagnosis and treatment of this disorder.

AIMS: We aimed to reveal the biomarkers associated with the development of IGD through resting-state brain network analysis and provide clues for the diagnosis and treatment of IGD.

METHODS: Twenty-six patients with IGD, 23 excessive internet game users (EIUs) who recurrently played internet games but were not diagnosed with IGD and 29 healthy controls (HCs) performed delay discounting task (DDT) and Iowa gambling task (IGT). Resting-state functional magnetic resonance imaging (fMRI) data were also collected.

RESULTS: Patients with IGD exhibited significantly lower hubness in the right medial orbital part of the superior frontal gyrus (ORBsupmed) than both the EIU and the HC groups. Additionally, the hubness of the right ORBsupmed was found to be positively correlated with the highest excessive internet gaming degree during the past year in the EIU group but not the IGD group; this might be the protective mechanism that prevents EIUs from becoming addicted to internet games. Moreover, the hubness of the right ORBsupmed was found to be related to the treatment outcome of patients with IGD, with higher hubness of this region indicating better recovery when undergoing forced abstinence. Further modelling analysis of the DDT and IGT showed that patients with IGD displayed higher impulsivity during the decision-making process, and impulsivity-related parameters were negatively correlated with the hubness of right ORBsupmed.

CONCLUSIONS: Our findings revealed that the impulsivity-related right ORBsupmed hubness could serve as a potential biomarker of IGD and provide clues for the diagnosis and treatment of IGD.

PMID:37583792 | PMC:PMC10423834 | DOI:10.1136/gpsych-2022-100985

Closing the loop between brain and electrical stimulation: towards precision neuromodulation treatments

Tue, 08/15/2023 - 18:00

Transl Psychiatry. 2023 Aug 14;13(1):279. doi: 10.1038/s41398-023-02565-5.

ABSTRACT

One of the most critical challenges in using noninvasive brain stimulation (NIBS) techniques for the treatment of psychiatric and neurologic disorders is inter- and intra-individual variability in response to NIBS. Response variations in previous findings suggest that the one-size-fits-all approach does not seem the most appropriate option for enhancing stimulation outcomes. While there is a growing body of evidence for the feasibility and effectiveness of individualized NIBS approaches, the optimal way to achieve this is yet to be determined. Transcranial electrical stimulation (tES) is one of the NIBS techniques showing promising results in modulating treatment outcomes in several psychiatric and neurologic disorders, but it faces the same challenge for individual optimization. With new computational and methodological advances, tES can be integrated with real-time functional magnetic resonance imaging (rtfMRI) to establish closed-loop tES-fMRI for individually optimized neuromodulation. Closed-loop tES-fMRI systems aim to optimize stimulation parameters based on minimizing differences between the model of the current brain state and the desired value to maximize the expected clinical outcome. The methodological space to optimize closed-loop tES fMRI for clinical applications includes (1) stimulation vs. data acquisition timing, (2) fMRI context (task-based or resting-state), (3) inherent brain oscillations, (4) dose-response function, (5) brain target trait and state and (6) optimization algorithm. Closed-loop tES-fMRI technology has several advantages over non-individualized or open-loop systems to reshape the future of neuromodulation with objective optimization in a clinically relevant context such as drug cue reactivity for substance use disorder considering both inter and intra-individual variations. Using multi-level brain and behavior measures as input and desired outcomes to individualize stimulation parameters provides a framework for designing personalized tES protocols in precision psychiatry.

PMID:37582922 | DOI:10.1038/s41398-023-02565-5

Trait repetitive negative thinking in depression is associated with functional connectivity in negative thinking state rather than resting state

Tue, 08/15/2023 - 18:00

J Affect Disord. 2023 Aug 13:S0165-0327(23)01031-5. doi: 10.1016/j.jad.2023.08.052. Online ahead of print.

ABSTRACT

Resting-state functional connectivity (RSFC) has been proposed as a potential indicator of repetitive negative thinking (RNT) in depression. However, identifying the specific functional process associated with RSFC alterations is challenging, and it remains unclear whether alterations in RSFC for depressed individuals are directly related to the RNT process or to individual characteristics distinct from the negative thinking process per se. To investigate the relationship between RSFC alterations and the RNT process in individuals with major depressive disorder (MDD), we compared RSFC with functional connectivity during an induced negative-thinking state (NTFC) in terms of their predictability of RNT traits and associated whole-brain connectivity patterns using connectome-based predictive modeling (CPM) and connectome-wide association (CWA) analyses. Thirty-six MDD participants and twenty-six healthy control participants underwent both resting state and induced negative thinking state fMRI scans. Both RSFC and NTFC distinguished between healthy and depressed individuals with CPM. However, trait RNT in depressed individuals, as measured by the Ruminative Responses Scale-Brooding subscale, was only predictable from NTFC, not from RSFC. CWA analysis revealed that negative thinking in depression was associated with higher functional connectivity between the default mode and executive control regions, which was not observed in RSFC. These findings suggest that RNT in depression involves an active mental process encompassing multiple brain regions across functional networks, which is not represented in the resting state. Although RSFC indicates brain functional alterations in MDD, they may not directly reflect the negative thinking process.

PMID:37582464 | DOI:10.1016/j.jad.2023.08.052

Spatial and Spectral Components of the BOLD Global Signal in Rat Resting-State Functional MRI

Tue, 08/15/2023 - 18:00

Magn Reson Med. 2023 Aug 15. doi: 10.1002/mrm.29824. Online ahead of print.

ABSTRACT

PURPOSE: In resting-state fMRI (rs-fMRI), the global signal average captures widespread fluctuations related to unwanted sources of variance such as motion and respiration, as well as widespread neural activity; however, relative contributions of neural and non-neural sources to the global signal remain poorly understood. This study sought to tackle this problem through the comparison of the BOLD global signal to an adjacent non-brain tissue signal, where neural activity was absent, from the same rs-fMRI scan obtained from anesthetized rats. In this dataset, motion was minimal and ventilation was phase-locked to image acquisition to minimize respiratory fluctuations. Data were acquired using three different anesthetics: isoflurane, dexmedetomidine, and a combination of dexmedetomidine and light isoflurane.

METHODS: A power spectral density estimate, a voxel-wise spatial correlation via Pearson's correlation, and a co-activation pattern analysis were performed using the global signal and the non-brain tissue signal. Functional connectivity was calculated using Pearson's linear correlation on default mode network (DMN) regions.

RESULTS: We report differences in the spectral composition of the two signals and show spatial selectivity within DMN structures that show an increased correlation to the global signal and decreased intra-network connectivity after global signal regression. All of the observed differences between the global signal and the non-brain tissue signal were maintained across anesthetics.

CONCLUSION: These results show that the global signal is distinct from the noise contained in the tissue signal, as support for a neural contribution. This study provides a unique perspective to the contents of the global signal and their origins.

PMID:37582301 | DOI:10.1002/mrm.29824

Connective differences between patients with depression with and without ASD: A case-control study

Tue, 08/15/2023 - 18:00

PLoS One. 2023 Aug 15;18(8):e0289735. doi: 10.1371/journal.pone.0289735. eCollection 2023.

ABSTRACT

BACKGROUND: Researchers find it difficult to distinguish between depression with ASD (Depress-wASD) and without ASD (Depression) in adult patients. We aimed to clarify the differences in brain connectivity between patients with depression with ASD and without ASD.

METHODS: From April 2017 to February 2019, 22 patients with suspected depression were admitted to the hospital for diagnosis or follow-up and met the inclusion criteria. The diagnosis was determined according to the Diagnostic and Statistical Manual of Mental Disorders-5 by skilled psychiatrists. The Hamilton Depression Rating Scale (HAM-D), Young Mania Raging Scale (YMRS), Mini-International Neuropsychiatric Interview, Parent-interview ASD Rating Scale-Text Revision (PARS-TR), and Autism-Spectrum Quotient-Japanese version (AQ-J) were used to assess the patients' background and help with diagnosis. Resting-state functional magnetic resonance imaging (rs-fMRI) was performed using the 3-T-MRI system. rs-fMRI was processed using the CONN functional connectivity toolbox. Voxel-based morphometry was performed using structural images.

RESULTS: No significant difference was observed between the Depress-wASD and Depression groups using the HAM-D, YMRS, AQ-J, Intelligence Quotient (IQ), and verbal IQ results. rs-fMRI for the Depress-wASD group indicated a positive connection between the salience network (SN) and right supramarginal gyrus (SMG) and a negative connection between the SN and hippocampus and para-hippocampus than that for the Depression group. No significant structural differences were observed between the groups.

CONCLUSIONS: We identified differences in the SN involving the SMG and hippocampal regions between the Depress-wASD and Depression groups.

PMID:37582068 | DOI:10.1371/journal.pone.0289735

Confounding Effects on the Performance of Machine Learning Analysis of Static Functional Connectivity Computed from rs-fMRI Multi-site Data

Tue, 08/15/2023 - 18:00

Neuroinformatics. 2023 Aug 15. doi: 10.1007/s12021-023-09639-1. Online ahead of print.

ABSTRACT

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive imaging technique widely used in neuroscience to understand the functional connectivity of the human brain. While rs-fMRI multi-site data can help to understand the inner working of the brain, the data acquisition and processing of this data has many challenges. One of the challenges is the variability of the data associated with different acquisitions sites, and different MRI machines vendors. Other factors such as population heterogeneity among different sites, with variables such as age and gender of the subjects, must also be considered. Given that most of the machine-learning models are developed using these rs-fMRI multi-site data sets, the intrinsic confounding effects can adversely affect the generalizability and reliability of these computational methods, as well as the imposition of upper limits on the classification scores. This work aims to identify the phenotypic and imaging variables producing the confounding effects, as well as to control these effects. Our goal is to maximize the classification scores obtained from the machine learning analysis of the Autism Brain Imaging Data Exchange (ABIDE) rs-fMRI multi-site data. To achieve this goal, we propose novel methods of stratification to produce homogeneous sub-samples of the 17 ABIDE sites, as well as the generation of new features from the static functional connectivity values, using multiple linear regression models, ComBat harmonization models, and normalization methods. The main results obtained with our statistical models and methods are an accuracy of 76.4%, sensitivity of 82.9%, and specificity of 77.0%, which are 8.8%, 20.5%, and 7.5% above the baseline classification scores obtained from the machine learning analysis of the static functional connectivity computed from the ABIDE rs-fMRI multi-site data.

PMID:37581850 | DOI:10.1007/s12021-023-09639-1

A Siamese Network with Node Convolution for Individualized Predictions Based on Connectivity Maps Extracted from Resting-State fMRI Data

Mon, 08/14/2023 - 18:00

IEEE J Biomed Health Inform. 2023 Aug 14;PP. doi: 10.1109/JBHI.2023.3304974. Online ahead of print.

ABSTRACT

Deep learning has demonstrated great potential for objective diagnosis of neuropsychiatric disorders based on neuroimaging data, which includes the promising resting-state functional magnetic resonance imaging (RS-fMRI). However, the insufficient sample size has long been a bottleneck for deep model training for the purpose. In this study, we proposed a Siamese network with node convolution (SNNC) for individualized predictions based on RS-fMRI data. With the involvement of Siamese network, which uses sample pair (rather than a single sample) as input, the problem of insufficient sample size can largely be alleviated. To adapt to connectivity maps extracted from RS-fMRI data, we applied node convolution to each of the two branches of the Siamese network. For regression purposes, we replaced the contrastive loss in classic Siamese network with the mean square error loss and thus enabled Siamese network to quantitatively predict label differences. The label of a test sample can be predicted based on any of the training samples, by adding the label of the training sample to the predicted label difference between them. The final prediction for a test sample in this study was made by averaging the predictions based on each of the training samples. The performance of the proposed SNNC was evaluated with age and IQ predictions based on a public dataset (Cam-CAN). The results indicated that SNNC can make effective predictions even with a sample size of as small as 40, and SNNC achieved state-of-the-art accuracy among a variety of deep models and standard machine learning approaches.

PMID:37578917 | DOI:10.1109/JBHI.2023.3304974

Altered functional connectivity of the thalamus and salience network in patients with cluster headache: a pilot study

Mon, 08/14/2023 - 18:00

Neurol Sci. 2023 Aug 14. doi: 10.1007/s10072-023-07011-4. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Previous studies have shown that the salience network (SN) and the thalamus are involved in cluster headache (CH) attacks. However, very little is known regarding the altered thalamus-SN functional connectivity in CH. The aim of this study was to explore alterations of functional connectivity between the thalamus and the SN in patients with CH to further gain insight into the pathophysiology of CH.

MATERIALS AND METHODS: The resting-state functional MRI (rs-fMRI) data of 21 patients with CH in the headache attack remission state during in-bout periods and 21 age- and sex-matched normal controls were obtained. The rs-fMRI data were analyzed by the independent component analysis (ICA) method, and the thalamus-SN functional connectivity in patients with right-sided and left-sided CH was compared with that in normal controls.

RESULTS: Decreased functional connectivity was found between the thalamus, both ipsilateral and contralateral to the headache side, and the SN during headache remission state in both right-sided CH patients and left-sided CH patients.

CONCLUSIONS: The findings suggest that the decreased functional connectivity between the thalamus and SN might be one of the pathologies underpinning the CH. This helps us to understand better the nature of the brain dysfunction in CH and the basic pathologies of CH, which implies that this deserves further investigation.

PMID:37578630 | DOI:10.1007/s10072-023-07011-4

Associations of Brain Entropy Estimated by Resting State fMRI With Physiological Indices, Body Mass Index, and Cognition

Mon, 08/14/2023 - 18:00

J Magn Reson Imaging. 2023 Aug 14. doi: 10.1002/jmri.28948. Online ahead of print.

ABSTRACT

BACKGROUND: In recent years, resting-state fMRI (rsfMRI)-based brain entropy (BEN) has gained increasing interest as a tool to characterize brain activity. While previous studies indicate that BEN is correlated with cognition, it remains unclear whether BEN is influenced by other factors that typically affect brain activity measured by fMRI.

PURPOSE: To investigate the relationship between BEN and physiological indices, including respiratory rate (RR), heart rate (HR), systolic blood pressure (s-BP), and body mass index (BMI), and to investigate whether and to what extent the relationship between BEN and cognition is influenced by physiological variables.

STUDY TYPE: Retrospective.

SUBJECTS: One thousand two hundred six healthy subjects (mean age: 28.83 ± 3.69 years; 550 male) with rsfMRI datasets selected from the Human Connectome Project (HCP).

FIELD STRENGTH/SEQUENCE: Multiband echo planar imaging (EPI) sequence at 3.0 Tesla.

ASSESSMENT: Neurocognitive, physical health (RR, HR, s-BP, BMI), and rsfMRI data were retrieved from the HCP datasets. Neurocognition was measured through the total cognition composite (TCC) score provided by HCP. BEN maps were calculated from rsfMRI data.

STATISTICAL TESTS: Multiple regression models, pheight -family wise error (FWE) < 0.05 and pcluster -FWE < 0.05 were considered statistically significant.

RESULTS: BEN was negatively associated with RR (T-thresholds ranging from 4.75 to 4.8; r-threshold = |0.15|) and positively associated with s-BP and BMI (T-thresholds ranging from 4.75 to 4.8; r-threshold = |0.15|) in areas overlapping with the default mode network. After controlling the physiological effects, BEN still showed regional associations with TCC, including negative associations (T-thresholds = 3.09; r-threshold = |0.1|) in the fronto-parietal cortex and positive associations (T-thresholds = 3.09; r-threshold = |0.1|) in the sensorimotor system (motor network and the limbic system).

DATA CONCLUSIONS: RR negatively affects rsfMRI-derived BEN, while s-BP and BMI positively affect BEN. The positive associations between BEN and cognition in the motor network and the limbic system might indicate a facilitation of information processing in the sensorimotor system.

EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

PMID:37578314 | DOI:10.1002/jmri.28948

Investigating Habenula Functional Connectivity and Reward-Related Activity in Obesity using Human Connectome Project Data

Mon, 08/14/2023 - 18:00

Brain Connect. 2023 Aug 14. doi: 10.1089/brain.2023.0034. Online ahead of print.

ABSTRACT

INTRODUCTION: The habenula, a brain region involved in aversion, might negatively modulate caloric intake. Functional Magnetic Resonance Imaging (fMRI) studies reported associations between weight loss and habenula functional connectivity. However, whether habenula resting state functional connectivity (rsFC) and reward-related activity is altered in obesity is yet unknown.

METHODS: Using data from the Human Connectome Project, we included 300 subjects with various BMIs and a healthy long-term blood glucose (HbA1c). Additionally, we investigated a potential BMI x HbA1c interaction in a separate cohort including subjects with prediabetes (n = 72). Habenula rsFC was assessed using a region of interest (ROI)-to-ROI analysis. Furthermore, a separate analysis using gambling task fMRI data focussed on reward-related habenular activity.

RESULTS: We did not find an association between BMI and habenular rsFC for any of the ROIs. For the exploratory analysis of the BMI x HbA1c effect, a significant interaction effect was found for the habenula-ventral tegmental area (VTA) connection, but this did not survive multiple comparisons correction. Monetary punishment compared to reward activated the bilateral habenula in the BMI sample, but this activity was not associated with BMI.

DISCUSSION: In conclusion, we did not find evidence for an association between BMI and habenula rsFC or reward-related activity. However, there might be an interaction between BMI and HbA1c for the habenula-VTA rsFC, suggestive of a role of habenula in glucose regulation. Future studies should focus on metabolic parameters in their experimental design, to confirm our findings and explore the precise role of the habenula in metabolism.

PMID:37578129 | DOI:10.1089/brain.2023.0034

Personalized brain MRI revealed distinct functional and anatomical disruptions in Creutzfeldt-Jakob disease and Alzheimer's disease

Mon, 08/14/2023 - 18:00

CNS Neurosci Ther. 2023 Aug 14. doi: 10.1111/cns.14404. Online ahead of print.

ABSTRACT

AIMS: Creutzfeldt-Jakob disease (CJD) is a lethal neurodegenerative disorder, which leads to a rapidly progressive dementia. This study aimed to examine the cortical alterations in CJD, changes in these brain characteristics over time, and the differences between CJD and Alzheimer's disease (AD) that show similar clinical manifestations.

METHODS: To obtain reliable, subject-specific functional measures, we acquired 24 min of resting-state fMRI data from each subject. We applied an individual-based approach to characterize the functional brain organization of 10 patients with CJD, 8 matched patients with AD, and 8 normal controls. We measured cortical atrophy as well as disruption in resting-state functional connectivity (rsFC) and then investigated longitudinal brain changes in a subset of CJD patients.

RESULTS: CJD was associated with widespread cortical thinning and weakened rsFC. Compared with AD, CJD showed distinct atrophy patterns and greater disruptions in rsFC. Moreover, the longitudinal data demonstrated that the progressive cortical thinning and disruption in rsFC mainly affected the association rather than the primary cortex in CJD.

CONCLUSIONS: CJD shows unique anatomical and functional disruptions in the cerebral cortex, distinct from AD. Rapid progression of CJD affects both the cortical thickness and rsFC in the association cortex.

PMID:37577861 | DOI:10.1111/cns.14404

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