Most recent paper

Molecular-enriched functional connectivity in the human brain using multiband multi-echo simultaneous ASL/BOLD fMRI

Thu, 07/20/2023 - 18:00

Sci Rep. 2023 Jul 20;13(1):11751. doi: 10.1038/s41598-023-38573-0.

ABSTRACT

Receptor-enriched analysis of functional connectivity by targets (REACT) is a strategy to enrich functional MRI (fMRI) data with molecular information on the neurotransmitter distribution density in the human brain, providing a biological basis to the functional connectivity (FC) analysis. Although this approach has been used in BOLD fMRI studies only so far, extending its use to ASL imaging would provide many advantages, including the more direct link of ASL with neuronal activity compared to BOLD and its suitability for pharmacological MRI studies assessing drug effects on baseline brain function. Here, we applied REACT to simultaneous ASL/BOLD resting-state fMRI data of 29 healthy subjects and estimated the ASL and BOLD FC maps related to six molecular systems. We then compared the ASL and BOLD FC maps in terms of spatial similarity, and evaluated and compared the test-retest reproducibility of each modality. We found robust spatial patterns of molecular-enriched FC for both modalities, moderate similarity between BOLD and ASL FC maps and comparable reproducibility for all but one molecular-enriched functional networks. Our findings showed that ASL is as informative as BOLD in detecting functional circuits associated with specific molecular pathways, and that the two modalities may provide complementary information related to these circuits.

PMID:37474568 | DOI:10.1038/s41598-023-38573-0

Functional alterations in bipartite network of white and grey matters during aging

Thu, 07/20/2023 - 18:00

Neuroimage. 2023 Jul 18:120277. doi: 10.1016/j.neuroimage.2023.120277. Online ahead of print.

ABSTRACT

The effects of normal aging on functional connectivity (FC) within various brain networks of grey matter (GM) have been well-documented. However, the age effects on the network of FC between white matter (WM) and GM, namely WM-GM FC, remains unclear. Evaluating crucial properties, such as global efficiency (GE), for a WM-GM FC network poses a challenge due to the absence of closed triangle paths which are essential for assessing network properties in traditional graph models. In this study, we propose a bipartite graph model to characterize the WM-GM FC network and quantify these challenging network properties. Leveraging this model, we assessed the WM-GM FC network properties at multiple scales across 1,462 cognitively normal subjects aged 22-96 years from three repositories (ADNI, BLSA and OASIS-3) and investigated the age effects on these properties throughout adulthood and during late adulthood (age ≥70 years). Our findings reveal that (1) heterogeneous alterations occurred in region-specific WM-GM FC over the adulthood and decline predominated during late adulthood; (2) the FC density of WM bundles engaged in memory, executive function and processing speed declined with age over adulthood, particularly in later years; and (3) the GE of attention, default, somatomotor, frontoparietal and limbic networks reduced with age over adulthood, and GE of visual network declined during late adulthood. These findings provide unpresented insights into multi-scale alterations in networks of WM-GM functional synchronizations during normal aging. Furthermore, our bipartite graph model offers an extendable framework for quantifying WM-engaged networks, which may contribute to a wide range of neuroscience research.

PMID:37473978 | DOI:10.1016/j.neuroimage.2023.120277

Multi-modal MRI measures reveal sensory abnormalities in major depressive disorder patients: A surface-based study

Thu, 07/20/2023 - 18:00

Neuroimage Clin. 2023 Jul 8;39:103468. doi: 10.1016/j.nicl.2023.103468. Online ahead of print.

ABSTRACT

BACKGROUND: Multi-modal magnetic resonance imaging (MRI) measures are supposed to be able to capture different brain neurobiological aspects of major depressive disorder (MDD). A fusion analysis of structural and functional modalities may better reveal the disease biomarker specific to the MDD disease.

METHODS: We recruited 30 MDD patients and 30 matched healthy controls (HC). For each subject, we acquired high-resolution brain structural images and resting-state fMRI (rs-fMRI) data using a 3 T MRI scanner. We first extracted the brain morphometric measures, including the cortical volume (CV), cortical thickness (CT), and surface area (SA), for each subject from the structural images, and then detected the structural clusters showing significant between-group differences in each measure using the surface-based morphology (SBM) analysis. By taking the identified structural clusters as seeds, we performed seed-based functional connectivity (FC) analyses to determine the regions with abnormal FC in the patients. Based on a logistic regression model, we performed a classification analysis by selecting these structural and functional cluster-wise measures as features to distinguish the MDD patients from the HC.

RESULTS: The MDD patients showed significantly lower CV in a cluster involving the right superior temporal gyrus (STG) and middle temporal gyrus (MTG), and lower SA in three clusters involving the bilateral STG, temporal pole gyrus, and entorhinal cortex, and the left inferior temporal gyrus, and fusiform gyrus, than the controls. No significant difference in CT was detected between the two groups. By taking the above-detected clusters as seeds to perform the seed-based FC analysis, we found that the MDD patients showed significantly lower FC between STG/MTG (CV's cluster) and two clusters located in the bilateral visual cortices than the controls. The logistic regression model based on the structural and functional features reached a classification accuracy of 86.7% (p < 0.001) between MDD and controls.

CONCLUSION: The present study showed sensory abnormalities in MDD patients using the multi-modal MRI analysis. This finding may act as a disease biomarker distinguishing MDD patients from healthy individuals.

PMID:37473494 | DOI:10.1016/j.nicl.2023.103468

Planning ahead: Predictable switching recruits task-active and resting-state networks

Thu, 07/20/2023 - 18:00

Hum Brain Mapp. 2023 Jul 20. doi: 10.1002/hbm.26430. Online ahead of print.

ABSTRACT

Switching is a difficult cognitive process characterised by costs in task performance; specifically, slowed responses and reduced accuracy. It is associated with the recruitment of a large coalition of task-positive regions including those referred to as the multiple demand cortex (MDC). The neural correlates of switching not only include the MDC, but occasionally the default mode network (DMN), a characteristically task-negative network. To unpick the role of the DMN during switching we collected fMRI data from 24 participants playing a switching paradigm that perturbed predictability (i.e., cognitive load) across three switch dimensions-sequential, perceptual, and spatial predictability. We computed the activity maps unique to switch vs. stay trials and all switch dimensions, then evaluated functional connectivity under these switch conditions by computing the pairwise mutual information functional connectivity (miFC) between regional timeseries. Switch trials exhibited an expected cost in reaction time while sequential predictability produced a significant benefit to task accuracy. Our results showed that switch trials recruited a broader activity map than stay trials, including regions of the DMN, the MDC, and task-positive networks such as visual, somatomotor, dorsal, salience/ventral attention networks. More sequentially predictable trials recruited increased activity in the somatomotor and salience/ventral attention networks. Notably, changes in sequential and perceptual predictability, but not spatial predictability, had significant effects on miFC. Increases in perceptual predictability related to decreased miFC between control, visual, somatomotor, and DMN regions, whereas increases in sequential predictability increased miFC between regions in the same networks, as well as regions within ventral attention/ salience, dorsal attention, limbic, and temporal parietal networks. These results provide novel clues as to how DMN may contribute to executive task performance. Specifically, the improved task performance, unique activity, and increased miFC associated with increased sequential predictability suggest that the DMN may coordinate more strongly with the MDC to generate a temporal schema of upcoming task events, which may attenuate switching costs.

PMID:37471699 | DOI:10.1002/hbm.26430

Relationship between temporal dynamics of intrinsic brain activity and motor function remodeling in patients with acute BGIS

Thu, 07/20/2023 - 18:00

Front Neurosci. 2023 Jun 27;17:1154018. doi: 10.3389/fnins.2023.1154018. eCollection 2023.

ABSTRACT

BACKGROUND: patients with acute basal ganglia ischemic stroke (BGIS) show changes in local brain activity represented by the amplitude of low-frequency fluctuation (ALFF), but the time-varying characteristics of this local nerve activity are still unclear. This study aimed to investigate the abnormal time-varying local brain activity of patients with acute BGIS by using the ALFF method combined with the sliding-window approach.

METHODS: In this study, 34 patients with acute BGIS with motor dysfunction and 44 healthy controls (HCs) were recruited. The dynamic amplitude of low-frequency fluctuation (dALFF) was employed to detect the alterations in brain activity induced by acute BGIS patients. A two-sample t-test comparison was performed to compare the dALFF value between the two groups and a Spearman correlation analysis was conducted to assess the relationship between the local brain activity abnormalities and clinical characteristics.

RESULTS: Compared with HCs, the activity of neurons in the left temporal pole (TP), parahippocampal gyrus (paraHIP), middle occipital gyrus (MOG), dorsolateral superior frontal gyrus (SFGdl), medial cingulate cortex (MCC), right rectus, precuneus (PCu) and right cerebellum crus1 were significantly increased in patients with BGIS. In addition, we found that there was a negative correlation (r = -0.458, p = 0.007) between the dALFF value of the right rectus and the scores of the National Institutes of Health Stroke Scale (NIHSS), and a positive correlation (r = 0.488, 0.499, p < 0.05) with the scores of the Barthel Index scale (BI) and the Fugl Meyer motor function assessment (FMA). ROC analysis results demonstrated that the area under the curves (AUC) of the right rectus was 0.880, p<0.001.

CONCLUSION: The pattern of intrinsic brain activity variability was altered in patients with acute BGIS compared with HCs. The abnormal dALFF variability might be a potential tool to assess motor function in patients with acute BGIS and potentially inform the diagnosis of this disease.

PMID:37469836 | PMC:PMC10353616 | DOI:10.3389/fnins.2023.1154018

Microglia dysfunction drives disrupted hippocampal amplitude of low frequency after acute kidney injury

Thu, 07/20/2023 - 18:00

CNS Neurosci Ther. 2023 Jul 19. doi: 10.1111/cns.14363. Online ahead of print.

ABSTRACT

AIMS: Acute kidney injury (AKI) has been associated with a variety of neurological problems, while the neurobiological mechanism remains unclear. In the present study, we utilized resting-state functional magnetic resonance imaging (rs-fMRI) to detect brain injury at an early stage and investigated the impact of microglia on the neuropathological mechanism of AKI.

METHODS: Rs-fMRI data were collected from AKI rats and the control group with a 9.4-Tesla scanner at 24, 48, and 72 h post administration of contrast medium or saline. The amplitude of low-frequency fluctuations (ALFF) was then compared across the groups at each time course. Additionally, flow cytometry and SMART-seq2 were employed to evaluate microglia. Furthermore, pathological staining and Western blot were used to analyze the samples.

RESULTS: MRI results revealed that AKI led to a decreased ALFF in the hippocampus, particularly in the 48 h and 72 h groups. Additionally, western blot suggested that AKI-induced the neuronal apoptosis at 48 h and 72 h. Flow cytometry and confocal microscopy images demonstrated that AKI activated the aggregation of microglia into neurons at 24 h, with a strong upregulation of M1 polarization at 48 h and peaking at 72 h, accompanying with the release of proinflammatory cytokines. The ALFF value was strongly correlated with the proportion of microglia (|r| > 0.80, p < 0.001).

CONCLUSIONS: Our study demonstrated that microglia aggregation and inflammatory factor upregulation are significant mechanisms of AKI-induced neuronal apoptosis. We used fMRI to detect the alterations in hippocampal function, which may provide a noninvasive method for the early detection of brain injury after AKI.

PMID:37469216 | DOI:10.1111/cns.14363

Classification of recurrent major depressive disorder using a new time series feature extraction method through multisite rs-fMRI data

Wed, 07/19/2023 - 18:00

J Affect Disord. 2023 Jul 17:S0165-0327(23)00928-X. doi: 10.1016/j.jad.2023.07.077. Online ahead of print.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) has a high rate of recurrence. Identifying patients with recurrent MDD is advantageous in adopting prevention strategies to reduce the disabling effects of depression.

METHOD: We propose a novel feature extraction method that includes dynamic temporal information, and inputs the extracted features into a graph convolutional network (GCN) to achieve classification of recurrent MDD. We extract the average time series using an atlas from resting-state functional magnetic resonance imaging (fMRI) data. Pearson correlation was calculated between brain region sequences at each time point, representing the functional connectivity at each time point. The connectivity is used as the adjacency matrix and the brain region sequences as node features for a GCN model to classify recurrent MDD. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to analyze the contribution of different brain regions to the model. Brain regions making greater contribution to classification were considered to be the regions with altered brain function in recurrent MDD.

RESULT: We achieved a classification accuracy of 75.8 % for recurrent MDD on the multi-site dataset, the Rest-meta-MDD. The brain regions closely related to recurrent MDD have been identified.

LIMITATION: The pre-processing stage may affect the final classification performance and harmonizing site differences may improve the classification performance.

CONCLUSION: The experimental results demonstrate that the proposed method can effectively classify recurrent MDD and extract dynamic changes of brain activity patterns in recurrent depression.

PMID:37467800 | DOI:10.1016/j.jad.2023.07.077

Partial entropy decomposition reveals higher-order information structures in human brain activity

Wed, 07/19/2023 - 18:00

Proc Natl Acad Sci U S A. 2023 Jul 25;120(30):e2300888120. doi: 10.1073/pnas.2300888120. Epub 2023 Jul 19.

ABSTRACT

The standard approach to modeling the human brain as a complex system is with a network, where the basic unit of interaction is a pairwise link between two brain regions. While powerful, this approach is limited by the inability to assess higher-order interactions involving three or more elements directly. In this work, we explore a method for capturing higher-order dependencies in multivariate data: the partial entropy decomposition (PED). Our approach decomposes the joint entropy of the whole system into a set of nonnegative atoms that describe the redundant, unique, and synergistic interactions that compose the system's structure. PED gives insight into the mathematics of functional connectivity and its limitation. When applied to resting-state fMRI data, we find robust evidence of higher-order synergies that are largely invisible to standard functional connectivity analyses. Our approach can also be localized in time, allowing a frame-by-frame analysis of how the distributions of redundancies and synergies change over the course of a recording. We find that different ensembles of regions can transiently change from being redundancy-dominated to synergy-dominated and that the temporal pattern is structured in time. These results provide strong evidence that there exists a large space of unexplored structures in human brain data that have been largely missed by a focus on bivariate network connectivity models. This synergistic structure is dynamic in time and likely will illuminate interesting links between brain and behavior. Beyond brain-specific application, the PED provides a very general approach for understanding higher-order structures in a variety of complex systems.

PMID:37467265 | DOI:10.1073/pnas.2300888120

Abnormal glutamatergic and serotonergic connectivity in visual snow syndrome and migraine with aura

Wed, 07/19/2023 - 18:00

Ann Neurol. 2023 Jul 19. doi: 10.1002/ana.26745. Online ahead of print.

ABSTRACT

OBJECTIVE: Neuropharmacological changes in visual snow syndrome (VSS) are poorly understood. We aimed to use receptor target maps combined with resting fMRI data to identify which neurotransmitters might modulate brain circuits involved in VSS.

METHODS: We used Receptor-Enriched Analysis of Functional Connectivity by Targets (REACT) to estimate and compare the molecular-enriched functional networks related to four neurotransmitter systems of patients with VSS (n = 24), healthy controls (HC, n = 24) and migraine patients (MIG, n = 25, n = 15 with aura-MwA). For REACT we employed receptor density templates for the transporters of noradrenaline, dopamine and serotonin, GABA-A and NMDA receptors, 5HT1B and 5HT2A receptors, and estimated the subject-specific voxel-wise maps of functional connectivity (FC). We then performed voxel-wise comparisons of these maps between HC, MIG and VSS.

RESULTS: VSS patients had reduced FC in glutamatergic networks localised in the ACC compared to HC and MIG, and reduced FC in serotoninergic networks localised in the insula, temporal pole and orbitofrontal cortex compared to controls, similar to MwA. VSS patients also showed reduced FC in 5HT2A -enriched networks, largely localised in occipito-temporo-parietal association cortices. As revealed by subgroup analyses, these changes were independent of, and analogous to, those found in MwA.

INTERPRETATION: Our results show that glutamate and serotonin are involved in brain connectivity alterations in areas of the visual, salience and limbic systems in VSS. Importantly, altered serotonergic connectivity is independent of migraine in VSS, and simultaneously comparable to that of migraine with aura, highlighting a shared biology between the disorders. This article is protected by copyright. All rights reserved.

PMID:37466404 | DOI:10.1002/ana.26745

Randomised placebo-controlled clinical trial evaluating the impact of a new visual rehabilitation program on neuroadaptation in patients implanted with trifocal intraocular lenses

Tue, 07/18/2023 - 18:00

Int Ophthalmol. 2023 Jul 18. doi: 10.1007/s10792-023-02809-9. Online ahead of print.

ABSTRACT

PURPOSE: To evaluate the efficacy of a new visual training program for improving the visual function in patients implanted with trifocal intraocular lenses (IOLs).

METHODS: Randomised placebo-controlled clinical trial enrolling 60 subjects (age, 47-75 years) undergoing cataract surgery with implantation of trifocal diffractive IOL. Home-based active visual training was prescribed immediately after surgery to all of them (20 sessions, 30 min): 31 subjects using a serious game based on Gabor patches (study group) and 29 using a placebo software (placebo group). Visual acuity, contrast sensitivity (CS), and perception of visual disturbances (QoV questionnaire) were evaluated before and after training. Likewise, in a small subgroup, resting-state functional magnetic resonance imaging (rs-fMRI) analysis was performed.

RESULTS: No significant differences were found between groups in compliance time (p = 0.70). After training, only significant improvements in monocular uncorrected intermediate visual acuity were found in the study group (p ≤ 0.01), although differences between groups did not reach statistical significance (p ≥ 0.11). Likewise, significantly better binocular far CS values were found in the study group for the spatial frequencies of 6 (p = 0.01) and 12 cpd (p = 0.03). More visual symptoms of the QoV questionnaire experienced a significant change in the level of bothersomeness in the study group. Rs-fMRI revealed the presence significant changes reflecting higher functional connectivity after the training with the serious game.

CONCLUSIONS: A 3-week visual training program based on the use of Gabor patches after bilateral implantation of trifocal diffractive IOLs may be beneficial for optimising the visual function, with neural changes associated suggesting an acceleration of neuroadaptation. Trial registration ClinicalTrials.gov, NCT04985097. Registered 02 August 2021, https://clinicaltrials.gov/(NCT04985097 ).

PMID:37464228 | DOI:10.1007/s10792-023-02809-9

Altered brain dynamic in major depressive disorder: state and trait features

Mon, 07/17/2023 - 18:00

Transl Psychiatry. 2023 Jul 17;13(1):261. doi: 10.1038/s41398-023-02540-0.

ABSTRACT

Temporal neural synchrony disruption can be linked to a variety of symptoms of major depressive disorder (MDD), including mood rigidity and the inability to break the cycle of negative emotion or attention biases. This might imply that altered dynamic neural synchrony may play a role in the persistence and exacerbation of MDD symptoms. Our study aimed to investigate the changes in whole-brain dynamic patterns of the brain functional connectivity and activity related to depression using the hidden Markov model (HMM) on resting-state functional magnetic resonance imaging (rs-fMRI) data. We compared the patterns of brain functional dynamics in a large sample of 314 patients with MDD (65.9% female; age (mean ± standard deviation): 35.9 ± 13.4) and 498 healthy controls (59.4% female; age: 34.0 ± 12.8). The HMM model was used to explain variations in rs-fMRI functional connectivity and averaged functional activity across the whole-brain by using a set of six unique recurring states. This study compared the proportion of time spent in each state and the average duration of visits to each state to assess stability between different groups. Compared to healthy controls, patients with MDD showed significantly higher proportional time spent and temporal stability in a state characterized by weak functional connectivity within and between all brain networks and relatively strong averaged functional activity of regions located in the somatosensory motor (SMN), salience (SN), and dorsal attention (DAN) networks. Both proportional time spent and temporal stability of this brain state was significantly associated with depression severity. Healthy controls, in contrast to the MDD group, showed proportional time spent and temporal stability in a state with relatively strong functional connectivity within and between all brain networks but weak averaged functional activity across the whole brain. These findings suggest that disrupted brain functional synchrony across time is present in MDD and associated with current depression severity.

PMID:37460460 | DOI:10.1038/s41398-023-02540-0

Functional connectivity and glutamate levels of the medial prefrontal cortex in schizotypy are related to sensory amplification in a probabilistic reasoning task

Mon, 07/17/2023 - 18:00

Neuroimage. 2023 Jul 15:120280. doi: 10.1016/j.neuroimage.2023.120280. Online ahead of print.

ABSTRACT

The circular inference (CI) computational model assumes a corruption of sensory data by prior information and vice versa, leading at the extremes to 'see what we expect' (through prior amplification) and/or to 'expect what we see' (through sensory amplification). Although a CI mechanism has been reported in a schizophrenia population, it has not been investigated in individuals experiencing psychosis-like experiences, such as people with high schizotypy traits. Furthermore, the neurobiological basis of CI, such as the link between hierarchical amplifications, excitatory neurotransmission, and resting state functional connectivity (RSFC), remains untested. The participants included in the present study consisted of a subsample of those recruited in a study previously published by our group, Kozhuharova et al., (2021).We included 36 participants with High (n=18) and Low (n=18) levels of schizotypy who completed a probabilistic reasoning task (the Fisher task) for which individual confidence levels were obtained and fitted to the CI model. Participants also underwent a 1H-Magnetic Resonance Spectroscopy (MRS) scan to measure medial prefrontal cortex (mPFC) glutamate metabolite levels, and a functional Magnetic Resonance Imaging (fMRI) scan to measure RSFC of the medial prefrontal cortex (mPFC). People with high levels of schizotypy exhibited changes in CI parameters, altered cortical excitatory neurotransmission and RSFC that were all associated with sensory amplification. Our findings capture a multimodal signature of CI that is observable in people early in the psychosis spectrum.

PMID:37460012 | DOI:10.1016/j.neuroimage.2023.120280

Disconnection of Network Hubs Underlying the Executive Function Deficit in Patients with Ischemic Leukoaraiosis

Mon, 07/17/2023 - 18:00

J Alzheimers Dis. 2023 Jul 12. doi: 10.3233/JAD-230048. Online ahead of print.

ABSTRACT

BACKGROUND: Cognitive impairment is the most common clinical manifestation of ischemic leukoaraiosis (ILA), but the underlying neurobiological pathways have not been well elucidated. Recently, it was thought that ILA is a "disconnection syndrome". Disorganized brain connectome were considered the key neuropathology underlying cognitive deficits in ILA patients.

OBJECTIVE: We aimed to detect the disruption of network hubs in ILA patients using a new analytical method called voxel-based eigenvector centrality (EC) mapping.

METHODS: Subjects with moderate to severe white matters hyperintensities (Fazekas score ≥3) and healthy controls (HCs) (Fazekas score = 0) were included in the study. The resting-state functional magnetic resonance imaging and the EC mapping approach were performed to explore the alteration of whole-brain network connectivity in ILA patients.

RESULTS: Relative to the HCs, the ILA patients exhibited poorer cognitive performance in episodic memory, information processing speed, and executive function (all ps < 0.0125). Additionally, compared with HCs, the ILA patients had lower functional connectivity (i.e., EC values) in the medial parts of default-mode network (i.e., bilateral posterior cingulate gyrus and ventral medial prefrontal cortex [vMPFC]). Intriguingly, the functional connectivity strength at the right vMPFC was positively correlated with executive function deficit in the ILA patients.

CONCLUSION: The findings suggested disorganization of the hierarchy of the default-mode regions within the whole-brain network in patients with ILA and advanced our understanding of the neurobiological mechanism underlying executive function deficit in ILA.

PMID:37458032 | DOI:10.3233/JAD-230048

Right inferior frontal gyrus theta-burst stimulation reduces smoking behaviors and strengthens fronto-striatal-limbic resting-state functional connectivity: a randomized crossover trial

Mon, 07/17/2023 - 18:00

Front Psychiatry. 2023 Jun 28;14:1166912. doi: 10.3389/fpsyt.2023.1166912. eCollection 2023.

ABSTRACT

INTRODUCTION: Functional and anatomical irregularities in the right inferior frontal gyrus (rIFG), a ventrolateral prefrontal region that mediates top-down inhibitory control over prepotent behavioral responding, are implicated in the ongoing maintenance of nicotine dependence (ND). However, there is little research on the effects of neuromodulation of the rIFG on smoking behavior, inhibitory control, and resting-state functional connectivity (rsFC) among individuals with ND.

METHODS: In this double-blind, crossover, theta-burst stimulation (TBS) study, adults with ND (N = 31; female: n = 15) completed a baseline session and were then randomized to two counterbalanced sessions of functionally neuronavigated TBS to the rIFG: continuous TBS (cTBS) on 1 day and intermittent TBS (iTBS) on another. Differences in cigarette cravings, smoking, and fronto-striatal-limbic rsFC were assessed.

RESULTS: Relative to baseline, cTBS significantly reduced appetitive and withdrawal cravings immediately after treatment. The effects of cTBS on withdrawal craving persisted for 24 h, as well as produced a reduction in smoking. Furthermore, cTBS significantly strengthened rsFC between the rIFG pars opercularis and subcallosal cingulate (fronto-striatal circuit), and between the rIFG pars opercularis and the right posterior parahippocampal gyrus (fronto-limbic circuit). At post-24 h, cTBS-induced increase in fronto-striatal rsFC was significantly associated with less appetitive craving, while the increase in fronto-limbic rsFC was significantly associated with less withdrawal craving and smoking.

DISCUSSION: These findings warrant further investigation into the potential value of rIFG cTBS to attenuate smoking behavior among individuals with ND.

PMID:37457779 | PMC:PMC10338839 | DOI:10.3389/fpsyt.2023.1166912

Brain age prediction using the graph neural network based on resting-state functional MRI in Alzheimer's disease

Mon, 07/17/2023 - 18:00

Front Neurosci. 2023 Jun 30;17:1222751. doi: 10.3389/fnins.2023.1222751. eCollection 2023.

ABSTRACT

INTRODUCTION: Alzheimer's disease (AD) is a neurodegenerative disease that significantly impacts the quality of life of patients and their families. Neuroimaging-driven brain age prediction has been proposed as a potential biomarker to detect mental disorders, such as AD, aiding in studying its effects on functional brain networks. Previous studies have shown that individuals with AD display impaired resting-state functional connections. However, most studies on brain age prediction have used structural magnetic resonance imaging (MRI), with limited studies based on resting-state functional MRI (rs-fMRI).

METHODS: In this study, we applied a graph neural network (GNN) model on controls to predict brain ages using rs-fMRI in patients with AD. We compared the performance of the GNN model with traditional machine learning models. Finally, the post hoc model was also used to identify the critical brain regions in AD.

RESULTS: The experimental results demonstrate that our GNN model can predict brain ages of normal controls using rs-fMRI data from the ADNI database. Moreover the differences between brain ages and chronological ages were more significant in AD patients than in normal controls. Our results also suggest that AD is associated with accelerated brain aging and that the GNN model based on resting-state functional connectivity is an effective tool for predicting brain age.

DISCUSSION: Our study provides evidence that rs-fMRI is a promising modality for brain age prediction in AD research, and the GNN model proves to be effective in predicting brain age. Furthermore, the effects of the hippocampus, parahippocampal gyrus, and amygdala on brain age prediction are verified.

PMID:37457008 | PMC:PMC10347411 | DOI:10.3389/fnins.2023.1222751

Exploring the correlation between genetic transcription and multi-temporal developmental autism spectrum disorder using resting-state functional magnetic resonance imaging

Mon, 07/17/2023 - 18:00

Front Neurosci. 2023 Jun 29;17:1219753. doi: 10.3389/fnins.2023.1219753. eCollection 2023.

ABSTRACT

INTRODUCTION: The present investigation aimed to explore the neurodevelopmental trajectory of autism spectrum disorder (ASD) by identifying the changes in brain function and gene expression associated with the disorder. Previous studies have indicated that ASD is a highly inherited neurodevelopmental disorder of the brain that displays symptom heterogeneity across different developmental periods. However, the transcriptomic changes underlying these developmental differences remain largely unknown.

METHODS: To address this gap in knowledge, our study employed resting-state functional magnetic resonance imaging (rs-fMRI) data from a large sample of male participants across four representative age groups to stratify the abnormal changes in brain function associated with ASD. Partial least square regression (PLSr) was utilized to identify unique changes in gene expression in brain regions characterized by aberrant functioning in ASD.

RESULTS: Our results revealed that ASD exhibits distinctive developmental trajectories in crucial brain regions such as the default mode network (DMN), temporal lobe, and prefrontal lobes during critical periods of neurodevelopment when compared to the control group. These changes were also associated with genes primarily located in synaptic tissues.

DISCUSSION: The findings of this study suggest that the neurobiology of ASD is uniquely heterogeneous across different ages and may be accompanied by distinct molecular mechanisms related to gene expression.

PMID:37456995 | PMC:PMC10339831 | DOI:10.3389/fnins.2023.1219753

Investigation of functional connectivity in Bell's palsy using functional magnetic resonance imaging: prospective cross-sectional study

Mon, 07/17/2023 - 18:00

Quant Imaging Med Surg. 2023 Jul 1;13(7):4676-4686. doi: 10.21037/qims-22-911. Epub 2023 Apr 13.

ABSTRACT

BACKGROUND: The most common cause of lower motor neuron facial palsy is Bell's palsy (BP). BP results in partial or complete inability to automatically move the facial muscles on the affected side and, in some cases, to close the eyelids, which can cause permanent eye damage. This study investigated changes in brain function and connectivity abnormalities in patients with BP.

METHODS: This study included 46 patients with unilateral BP and 34 healthy controls (HCs). Resting-state brain functional magnetic resonance imaging (fMRI) images were acquired, and Toronto Facial Grading System (TFGS) scores were obtained for all participants. The fractional amplitude of low-frequency fluctuation (fALFF) was estimated, and the relationship between the TFGS and fALFF was determined using correlation analysis for brain regions with changes in fALFF in those with BP versus HCs. Brain regions associated with TFGS were used as seeds for further functional connectivity (FC) analysis; relationships between FC values of abnormal areas and TFGS scores were also analyzed.

RESULTS: Activation of the right precuneus, right angular gyrus, left supramarginal gyrus, and left middle occipital gyrus was significantly decreased in the BP group. fALFF was significantly higher in the right thalamus, vermis, and cerebellum of the BP group compared with that in the HC group (P<0.05). The FC between the left middle occipital gyrus and right angular gyrus, left precuneus, and right middle frontal gyrus increased sharply, but decreased in the left angular gyrus, left posterior cingulate gyrus, left middle frontal gyrus, inferior cerebellum, and left middle temporal gyrus. Furthermore, the fALFF in the left middle occipital gyrus was negatively correlated with TFGS score (R=0.144; P=0.008).

CONCLUSIONS: The pathogenesis of BP is closely related to functional reorganization of the cerebral cortex. Patients with BP have altered fALFF activity in cortical regions associated with facial motion feedback monitoring.

PMID:37456292 | PMC:PMC10347328 | DOI:10.21037/qims-22-911

Three potential neurovascular pathways driving the benefits of mindfulness meditation for older adults

Mon, 07/17/2023 - 18:00

Front Aging Neurosci. 2023 Jun 29;15:1207012. doi: 10.3389/fnagi.2023.1207012. eCollection 2023.

ABSTRACT

Mindfulness meditation has been shown to be beneficial for a range of different health conditions, impacts brain function and structure relatively quickly, and has shown promise with aging samples. Functional magnetic resonance imaging metrics provide insight into neurovascular health which plays a key role in both normal and pathological aging processes. Experimental mindfulness meditation studies that included functional magnetic resonance metrics as an outcome measure may point to potential neurovascular mechanisms of action relevant for aging adults that have not yet been previously examined. We first review the resting-state magnetic resonance studies conducted in exclusively older adult age samples. Findings from older adult-only samples are then used to frame the findings of task magnetic resonance imaging studies conducted in both clinical and healthy adult samples. Based on the resting-state studies in older adults and the task magnetic resonance studies in adult samples, we propose three potential mechanisms by which mindfulness meditation may offer a neurovascular therapeutic benefit for older adults: (1) a direct neurovascular mechanism via increased resting-state cerebral blood flow; (2) an indirect anti-neuroinflammatory mechanism via increased functional connectivity within the default mode network, and (3) a top-down control mechanism that likely reflects both a direct and an indirect neurovascular pathway.

PMID:37455940 | PMC:PMC10340530 | DOI:10.3389/fnagi.2023.1207012

Changes in white matter functional networks across late adulthood

Mon, 07/17/2023 - 18:00

Front Aging Neurosci. 2023 Jun 30;15:1204301. doi: 10.3389/fnagi.2023.1204301. eCollection 2023.

ABSTRACT

INTRODUCTION: The aging brain is characterized by decreases in not only neuronal density but also reductions in myelinated white matter (WM) fibers that provide the essential foundation for communication between cortical regions. Age-related degeneration of WM has been previously characterized by histopathology as well as T2 FLAIR and diffusion MRI. Recent studies have consistently shown that BOLD (blood oxygenation level dependent) effects in WM are robustly detectable, are modulated by neural activities, and thus represent a complementary window into the functional organization of the brain. However, there have been no previous systematic studies of whether or how WM BOLD signals vary with normal aging. We therefore performed a comprehensive quantification of WM BOLD signals across scales to evaluate their potential as indicators of functional changes that arise with aging.

METHODS: By using spatial independent component analysis (ICA) of BOLD signals acquired in a resting state, WM voxels were grouped into spatially distinct functional units. The functional connectivities (FCs) within and among those units were measured and their relationships with aging were assessed. On a larger spatial scale, a graph was reconstructed based on the pair-wise connectivities among units, modeling the WM as a complex network and producing a set of graph-theoretical metrics.

RESULTS: The spectral powers that reflect the intensities of BOLD signals were found to be significantly affected by aging across more than half of the WM units. The functional connectivities (FCs) within and among those units were found to decrease significantly with aging. We observed a widespread reduction of graph-theoretical metrics, suggesting a decrease in the ability to exchange information between remote WM regions with aging.

DISCUSSION: Our findings converge to support the notion that WM BOLD signals in specific regions, and their interactions with other regions, have the potential to serve as imaging markers of aging.

PMID:37455933 | PMC:PMC10347529 | DOI:10.3389/fnagi.2023.1204301

Connectome-based fingerprint of motor impairment is stable along the course of Parkinson's disease

Mon, 07/17/2023 - 18:00

Cereb Cortex. 2023 Jul 15:bhad252. doi: 10.1093/cercor/bhad252. Online ahead of print.

ABSTRACT

Functional alterations in brain connectivity have previously been described in Parkinson's disease, but it is not clear whether individual differences in connectivity profiles might be also linked to severity of motor-symptom manifestation. Here we investigated the relevance of individual functional connectivity patterns measured with resting-state fMRI with respect to motor-symptom severity in Parkinson's disease, through a whole-brain, data-driven approach (connectome-based predictive modeling). Neuroimaging and clinical data of Parkinson's disease patients from the Parkinson's Progression Markers Initiative were derived at baseline (session 1, n = 81) and at follow-up (session 2, n = 53). Connectome-based predictive modeling protocol was implemented to predict levels of motor impairment from individual connectivity profiles. The resulting predictive model comprised a network mainly involving functional connections between regions located in the cerebellum, and in the motor and frontoparietal networks. The predictive power of the model was stable along disease progression, as the connectivity within the same network could predict levels of motor impairment, even at a later stage of the disease. Finally, connectivity profiles within this network could be identified at the individual level, suggesting the presence of individual fingerprints within resting-state fMRI connectivity associated with motor manifestations in Parkinson's disease.

PMID:37455441 | DOI:10.1093/cercor/bhad252

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