Most recent paper

Front Neurosci. 2024 Sep 27;18:1477181. doi: 10.3389/fnins.2024.1477181. eCollection 2024.

ABSTRACT

BACKGROUND: Traditional Chinese opera, such as Pingju, requires actors to master sophisticated performance skills and cultural knowledge, potentially influencing brain function. This study aimed to explore the effects of long-term opera training on the dynamic amplitude of low-frequency fluctuation (dALFF) and dynamic functional connectivity (dFC).

METHODS: Twenty professional well-trained Pingju actors and twenty demographically matched untrained subjects were recruited. Resting-state functional magnetic resonance imaging (fMRI) data were collected to assess dALFF differences in spontaneous regional brain activity between the actors and untrained participants. Brain regions with altered dALFF were selected as the seeds for the subsequent dFC analysis. Statistical comparisons examined differences between groups, while correlation analyses explored the relationships between dALFF and dFC, as well as the associations between these neural measures and the duration of Pingju training.

RESULTS: Compared with untrained subjects, professional Pingju actors exhibited significantly lower dALFF in the right lingual gyrus. Additionally, actors showed increased dFC between the right lingual gyrus and the bilateral cerebellum, as well as between the right lingual gyrus and the bilateral midbrain/red nucleus/thalamus, compared with untrained subjects. Furthermore, a negative correlation was found between the dALFF in the right lingual gyrus and its dFC, and a significant association was found between dFC in the bilateral midbrain/red nucleus/thalamus and the duration of Pingju training.

CONCLUSION: Long-term engagement in Pingju training induces neuroplastic changes, reflected in altered dALFF and dFC. These findings provide evidence for the interaction between artistic training and brain function, highlighting the need for further research into the impact of professional training on cognitive functions.

PMID:39399381 | PMC:PMC11466935 | DOI:10.3389/fnins.2024.1477181

ArXiv [Preprint]. 2024 Aug 22:arXiv:2406.05248v3.

ABSTRACT

FMRI data are noisy, complicated to acquire, and typically go through many steps of processing before they are used in a study or clinical practice. Being able to visualize and understand the data from the start through the completion of processing, while being confident that each intermediate step was successful, is challenging. AFNI's afni_proc$.$py is a tool to create and run a processing pipeline for FMRI data. With its flexible features, afni_proc$.$py allows users to both control and evaluate their processing at a detailed level. It has been designed to keep users informed about all processing steps: it does not just process the data, but first outputs a fully commented processing script that the users can read, query, interpret and refer back to. Having this full provenance is important for being able to understand each step of processing; it also promotes transparency and reproducibility by keeping the record of individual-level processing and modeling specifics in a single, shareable place. Additionally, afni_proc$.$py creates pipelines that contain several automatic self-checks for potential problems during runtime. The output directory contains a dictionary of relevant quantities that can be programmatically queried for potential issues and a systematic, interactive quality control (QC) HTML. All of these features help users evaluate and understand their data and processing in detail. We describe these and other aspects of afni_proc$.$py here using a set of task-based and resting state FMRI example commands.

PMID:39398207 | PMC:PMC11468194

Brain Topogr. 2024 Oct 13;38(1):6. doi: 10.1007/s10548-024-01079-7.

ABSTRACT

The aim of this study is to investigate activity and functional connectivity (FC) of Papez circuit networks associated with music processing using functional magnetic resonance imaging (fMRI) in depressed breast cancer patients. Twenty-three breast cancer patients listened to four different Iranian/Persian music paradigms during the resting-state fMRI scanning session: negative stimulation of traditional music, negative stimulation of pop music, positive stimulation of traditional music and positive stimulation of pop music. The amplitude of low-frequency fluctuation (ALFF) was used to evaluate the local characteristics of spontaneous brain activity. FC maps were created using multivariate ROI-to-ROI connectivity (mRRC) and Papez circuit-based regions of interest (ROIs) selection. We found that music increases FC within various brain networks which are involved in memory, emotion, and cognitive function, including the limbic system, the default mode network (DMN), salience network (SN), and central executive network (CEN). Moreover, it seems that the traditional types (both positive and negative) of Iranian music may be more effective to affect brain activity in the patients with breast cancer, than the Iranian pop music. These findings demonstrate that music therapy, as an effective and easily applicable approach, supports the neuropsychological recovery and can contribute to standard treatment protocols in patients with breast cancer.

PMID:39397183 | DOI:10.1007/s10548-024-01079-7

Nutr Res. 2024 Jul 23;131:147-158. doi: 10.1016/j.nutres.2024.07.004. Online ahead of print.

ABSTRACT

Maternal nutrition during the perinatal stage is critical to offspring brain development. Egg yolks are a balanced and nutrient-dense food that is rich in bioactive components crucial to optimal neurodevelopment early in life. Egg consumption is often recommended to pregnant women to enhance both maternal and fetal health. We hypothesized that maternal intake of egg yolk from late gestation and throughout lactation would enhance functional organization and cognitive developmental outcomes in offspring using a pig model. Sows were fed a control diet (n = 6) or a diet containing egg yolks (n = 5, 350 mg egg yolk powder/kg BW/day, equivalent to ∼3 eggs/day for humans) from late gestation through lactation. At weaning, piglet offspring (n = 2/sow, total n = 22) underwent structural magnetic resonance imaging (MRI) and resting-state-functional MRI. Piglets underwent novel object recognition testing to assess hippocampal-dependent learning and memory. Functional MRI results demonstrated that egg yolk significantly increased functional activation in the executive network (p = 0.0343) and cerebellar network (p = 0.0253) in piglets when compared to control. Diffusion tensor imaging analysis showed that perinatal intake of egg yolks significantly increased white matter fiber length in the hippocampus (p = 0.0363) and cerebellum (p = 0.0287) in piglet offspring compared to control piglets. Furthermore, piglets from egg yolk-fed sows spent significantly more proportional frequency exploring the novel object than the familiar object in novel object recognition testing (p = 0.0370). The findings from this study support egg yolk-altered activation of specific brain networks may be associated with functional cognitive outcomes in weaning piglets.

PMID:39395250 | DOI:10.1016/j.nutres.2024.07.004

Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi. 2024 Sep 20;42(9):689-694. doi: 10.3760/cma.j.cn121094-20231122-00126.

ABSTRACT

Objective: To investigate the changes of directional connections of auditory and non-auditory in patients with noise-induced deafness (NID) by degree centrality (DC) and Granger causality analysis (GCA), and to explore the mode of brain function remodeling after NID. Methods: In October 2023, a total of 58 patients diagnosed with NID by the Occupational Diseases Department of Yantaishan Hospital of Yantai from 2014 to 2022 were collected as case group (NID group), and 42 healthy volunteers matched by gender, age and education level were selected as the control group (HC group). Resting state-functional magnetic resonance imaging (Rs-fMRI) was perfomed and PC analysis was performed. The brain regions with statistically significant differences in DC values between groups and the bilateral Heschl regions were extracted as regions of interest (ROI) for voxel-based whole brain GCA and correlation analysis. Results: Compared with HC group, the SOG.L DC value of NID group was lower, the connectivity values of SFGdor.L to SOG.L was increased, the connectivity value of PCL.L to SOG.L was decreased, the connectivity values of ORBmid.L, PCG.R and CUN. L/R to HES.L were increased, the connectivity value of SFGdor.L to HES.L was decreased, the connectivity value of HES.L to PCUN.L was decreased, the connectivity values of ORBsup.L and PCG.R to HES.R were increased, the connectivity value of HES.R to CUN.L was decreased (P voxel level<0.01, P cluster level<0.05). The connectivity value of PCL.L to SOG.L was negatively correlated with the weighted value of the better whisper frequency (P<0.05) . Conclusion: The NID patients have abnormal directional connectivity activity in multiple brain regions, such as auditory vision, executive control, somatosensory movement, and default mode network. It is suggested that hearing loss may cause complex neural remodeling between auditory and non-auditory centers.

PMID:39394708 | DOI:10.3760/cma.j.cn121094-20231122-00126

Brain Res. 2024 Oct 9:149265. doi: 10.1016/j.brainres.2024.149265. Online ahead of print.

ABSTRACT

The networks observed in the brain during resting-state activity are not entirely "task-free." Instead, they hint at a hierarchical structure prepared for adaptive cognitive functions. Recent studies have increasingly demonstrated the potential of resting-state fMRI to predict local activations or global connectomes during task performance. However, uncertainties remain regarding the unique and shared task-specific components within resting-state brain networks, elucidating local activations and global connectome patterns. A coherent framework is also required to integrate these task-specific components to predict local activations and global connectome patterns. In this work, we introduce the Rest2Task model based on the partial least squares-based multivariate regression algorithm, which effectively integrates mappings from resting-state connectivity to local activations and global connectome patterns. By analyzing the coefficients of the regression model, we extracted task-specific resting-state components corresponding to brain local activation or global connectome of various tasks and applied them to the brain lateralization prediction and psychiatric disorders diagnostic. Our model effectively substitutes traditional whole-brain functional connectivity (FC) in predicting functional lateralization and diagnosing brain disorders. Our research represents the inaugural effort to quantify the contribution of patterns (components) within resting-state FC to different tasks, endowing these components with specific task-related contextual information. The task-specific resting-state components offer new insights into brain lateralization processing and disease diagnosis, potentially providing fresh perspectives on the adaptive transformation of brain networks in response to tasks.

PMID:39393483 | DOI:10.1016/j.brainres.2024.149265

J Biomed Phys Eng. 2024 Oct 1;14(5):435-446. doi: 10.31661/jbpe.v0i0.2204-1481. eCollection 2024 Oct.

ABSTRACT

BACKGROUND: Non-specific chronic low back pain (CLBP) is a common painful condition and is responsible for different physical disorders. Despite alternative therapies, patients still suffer from persistent pain. Repetitive transcranial magnetic stimulation (rTMS) has provided much evidence of pain reduction, but results have not been examined deeply in CLBP symptoms.

OBJECTIVE: The analgesic effect of rTMS in non-specific CLBP patients was evaluated by the amplitude of low-frequency fluctuation (ALFF) analysis in resting-state fMRI.

MATERIAL AND METHODS: In this experimental study, fifteen non-specific CLBP participants (46.87±10.89 years) received 20 Hz rTMS over the motor cortex. The pain intensity and brain functional scan were obtained during pre and post-stimulation for all participants. The ALFF maps of the brain in two scan sessions were identified and the percentage of pain reduction (PPR%) was determined using paired t-test. Also, correlation analysis was used to find a relationship between ALFFs and pain intensity.

RESULTS: Pain intensity was significantly reduced after induced-rTMS in non-specific CLBP (36.22%±13.28, P<0.05). Positive correlation was found between ALFF in the insula (INS) and pain intensity (rpre-rTMS=0.59, rpost-rTMS=0.58) while ALFF in medial prefrontal cortex (mPFC) and pain intensity had negatively correlated (rpre-rTMS=-0.54, rpost-rTMS=-0.56) (P<0.05). ALFF increased in mPFC while INS, thalamus (THA), and supplementary motor area (SMA) showed decremental ALFF followed by rTMS.

CONCLUSION: This study demonstrated that ALFF in INS, THA, mPFC, and SMA is associated with CLBP symptoms and analgesic effects of rTMS. ALFF potentially seems to be a proper objective neuroimaging parameter to link spontaneous brain activity with pain intensity in non-specific CLBP patients.

PMID:39391282 | PMC:PMC11462276 | DOI:10.31661/jbpe.v0i0.2204-1481

bioRxiv [Preprint]. 2024 Sep 26:2024.09.26.615215. doi: 10.1101/2024.09.26.615215.

ABSTRACT

Adolescence is a period of growth in cognitive performance and functioning. Recently, data-driven measures of brain-age gap, which can index cognitive decline in older populations, have been utilized in adolescent data with mixed findings. Instead of using a data-driven approach, here we assess the maturation status of the brain functional landscape in early adolescence by directly comparing an individual's resting-state functional connectivity (rsFC) to the canonical early-life and adulthood communities. Specifically, we hypothesized that the degree to which a youth's connectome is better captured by adult networks compared to infant/toddler networks is predictive of their cognitive development. To test this hypothesis across individuals and longitudinally, we utilized the Adolescent Brain Cognitive Development (ABCD) Study at baseline (9-10 years; n = 6,489) and 2-year-follow-up (Y2: 11-12 years; n = 5,089). Adjusted for demographic factors, our anchored rsFC score (AFC) was associated with better task performance both across and within participants. AFC was related to age and aging across youth, and change in AFC statistically mediated the age-related change in task performance. In conclusion, we showed that a model-fitting-free index of the brain at rest that is anchored to both adult and baby connectivity landscapes predicts cognitive performance and development in youth.

PMID:39386610 | PMC:PMC11463351 | DOI:10.1101/2024.09.26.615215

bioRxiv [Preprint]. 2024 Sep 27:2024.02.19.581086. doi: 10.1101/2024.02.19.581086.

ABSTRACT

Brain function can be modeled as the dynamic interactions between functional sources at different spatial scales, and each spatial scale can contain its functional sources with unique information, thus using a single scale may provide an incomplete view of brain function. This paper introduces a novel approach, termed "telescopic independent component analysis (TICA)," designed to construct spatial functional hierarchies and estimate functional sources across multiple spatial scales using fMRI data. The method employs a recursive ICA strategy, leveraging information from a larger network to guide the extraction of information about smaller networks. We apply our model to the default mode network (DMN), visual network (VN), and right frontoparietal network (RFPN). We investigate further on DMN by evaluating the difference between healthy people and individuals with schizophrenia. We show that the TICA approach can detect the spatial hierarchy of DMN, VS, and RFPN. In addition, TICA revealed DMN-associated group differences between cohorts that may not be captured if we focus on a single-scale ICA. In sum, our proposed approach represents a promising new tool for studying functional sources.

PMID:39386484 | PMC:PMC11463639 | DOI:10.1101/2024.02.19.581086

Brain Imaging Behav. 2024 Oct 10. doi: 10.1007/s11682-024-00949-9. Online ahead of print.

ABSTRACT

Patients with insomnia disorder (ID) usually experience a greater burden of comorbid anxiety symptoms. However, the neural mechanism under the mutual relationship between ID and anxiety remains largely unclear. The meta-analysis aimed to explore the concordance and distinction of regional brain functional activity in patients with ID and those with generalized anxiety disorder (GAD) using coordinate-based activation likelihood estimation approach. Studies using resting-state regional homogeneity, amplitude of low-frequency fluctuations (ALFF), or fractional ALFF in patients with ID or GAD were included by searching multiple databases up to May 24, 2024. Using meta-analytic approach, 21 studies of ID vs. healthy controls (HC) and 16 studies of GAD vs. HC were included to illuminate the common and distinct patterns between the two disorders. Results showed that ID and GAD shared increased brain activities in the left posterior cingulate cortex and left precuneus, as well as decreased brain activity in the left medial prefrontal cortex. Additionally, compared with ID, GAD showed greater increased activities in the left superior frontal gyrus. Our study reveals both common and different activation patterns between ID and GAD, which may provide novel insights for understanding the neural basis of the two disorders and enlighten the possibility of the development of more targeted treatment strategies for ID and GAD.

PMID:39388008 | DOI:10.1007/s11682-024-00949-9

BMC Psychiatry. 2024 Oct 10;24(1):671. doi: 10.1186/s12888-024-06112-4.

ABSTRACT

BACKGROUND: Individuals using methamphetamine (METH) may experience psychosis, which usually requires aggressive treatment. Studies of the neural correlates of METH-associated psychosis (MAP) have focused predominantly on the default mode network (DMN) and cognitive control networks. We hypothesize that METH use alters global functional connections in resting-state brain networks and that certain cross-network connections could be associated with psychosis.

METHODS: We recruited 24 healthy controls (CRL) and 54 men with METH use disorder (MUD) who were then divided into 25 without psychosis (MNP) and 29 with MAP. Psychotic symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS), evaluating (1) large-scale alterations in regional-wise resting-state functional connectivity (rsFC) across 11 brain networks and (2) associations between rsFC and psychotic symptom severity.

RESULTS: The MUD group exhibited greater rsFC between the salience network (SN)-DMN, and subcortical network (SCN)-DMN compared to the CRL group. The MAP group exhibited decreased rsFC in the sensory/somatomotor network (SMN)-dorsal attention network (DAN), SMN-ventral attention network (VAN), SMN-SN, and SMN-auditory network (AN), whereas the MNP group exhibited increased rsFC in the SMN-DMN and the frontoparietal network (FPN)-DMN compared to CRL. Additionally, the MAP group exhibited decreased rsFC strength between the SMN-DMN, SMN-AN, SMN-FPN, and DMN-VAN compared to the MNP group. Furthermore, across the entire MUD group, the PANSS-Positive subscale was negatively correlated with the DMN-FPN and FPN-SMN, while the PANSS-Negative subscale was negatively correlated with the DMN-AN and SMN-SMN.

CONCLUSION: MUD is associated with altered global functional connectivity. In addition, the MAP group exhibits a different brain functional network compared to the MNP group.

PMID:39390430 | PMC:PMC11468263 | DOI:10.1186/s12888-024-06112-4

Nutr Diabetes. 2024 Oct 10;14(1):86. doi: 10.1038/s41387-024-00343-w.

ABSTRACT

BACKGROUND: Quantitative mapping of the brain's metabolism is a critical tool in studying and diagnosing many conditions, from obesity to neurodegenerative diseases. In particular, noninvasive approaches are urgently required. Recently, there have been promising drug development approaches for the treatment of disorders related to glucose metabolism in the brain and, therefore, against obesity-associated diseases. One of the most important drug targets to emerge has been the Glucagon-like peptide-1 (GLP-1) and its receptor (GLP-1R). GLP and GLP-1R play an important role in regulating blood sugar and maintaining energy homeostasis. However, the macroscopic effects on brain metabolism and function due to the presence of GLP-1R are unclear.

METHODS: To explore the physiological role of GLP-1R in mouse brain glucose metabolism, and its relationship to brain function, we used three methods. We used deuterium magnetic resonance spectroscopy (DMRS) to provide quantitative information about metabolic flux, fluorodeoxyglucose positron emission tomography (FDG-PET) to measure brain glucose metabolism, and resting state-functional MRI (rs-fMRI) to measure brain functional connectivity. We used these methods in both mice with complete GLP-1R knockout (GLP-1R KO) and wild-type C57BL/6N (WT) mice.

RESULTS: The metabolic rate of GLP-1R KO mice was significantly slower than that of WT mice (p = 0.0345, WT mice 0.02335 ± 0.057 mM/min, GLP-1R KO mice 0.01998 ± 0.07 mM/min). Quantification of the mean [18F]FDG signal in the whole brain also showed significantly reduced glucose uptake in GLP-1R KO mice versus control mice (p = 0.0314). Observing rs-fMRI, the functional brain connectivity in GLP-1R KO mice was significantly lower than that in the WT group (p = 0.0032 for gFCD, p = 0.0002 for whole-brain correlation, p < 0.0001 for ALFF).

CONCLUSIONS: GLP-1R KO mice exhibit impaired brain glucose metabolism to high doses of exogenous glucose, and they also have reduced functional connectivity. This suggests that the GLP-1R KO mouse model may serve as a model for correlated metabolic and functional connectivity loss.

PMID:39389952 | PMC:PMC11466955 | DOI:10.1038/s41387-024-00343-w

Front Aging Neurosci. 2024 Sep 25;16:1455020. doi: 10.3389/fnagi.2024.1455020. eCollection 2024.

ABSTRACT

BACKGROUND: Olfactory dysfunction stands as one of the most prevalent non-motor symptoms in the initial stage of Parkinson's disease (PD). Nevertheless, the intricate mechanisms underlying olfactory deficits in Parkinson's disease still remain elusive.

METHODS: This study collected rs-fMRI data from 30 PD patients [15 with severe hyposmia (PD-SH) and 15 with no/mild hyposmia (PD-N/MH)] and 15 healthy controls (HC). To investigate functional segregation, the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) were utilized. Functional connectivity (FC) analysis was performed to explore the functional integration across diverse brain regions. Additionally, the graph theory-based network analysis was employed to assess functional networks in PD patients. Furthermore, Pearson correlation analysis was conducted to delve deeper into the relationship between the severity of olfactory dysfunction and various functional metrics.

RESULTS: We discovered pronounced variations in ALFF, ReHo, FC, and topological brain network attributes across the three groups, with several of these disparities exhibiting a correlation with olfactory scores.

CONCLUSION: Using fMRI, our study analyzed brain function in PD-SH, PD-N/MH, and HC groups, revealing impaired segregation and integration in PD-SH and PD-N/MH. We hypothesize that changes in temporal, frontal, occipital, and cerebellar activities, along with aberrant cerebellum-insula connectivity and node degree and betweenness disparities, may be linked to olfactory dysfunction in PD patients.

PMID:39385833 | PMC:PMC11461260 | DOI:10.3389/fnagi.2024.1455020

Front Aging Neurosci. 2024 Sep 25;16:1408685. doi: 10.3389/fnagi.2024.1408685. eCollection 2024.

ABSTRACT

BACKGROUND: Voxel-mirrored homotopic connectivity (VMHC) is utilized to assess the functional connectivity of neural networks by quantifying the similarity between corresponding regions in the bilateral hemispheres of the brain. The exploration of VMHC abnormalities in basal ganglia ischemic stroke (BGIS) patients across different cerebral hemispheres has been limited. This study seeks to establish a foundation for understanding the functional connectivity status of both brain hemispheres in BGIS patients through the utilization of VMHC analysis utilizing resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS: This study examined a total of 38 patients with left basal ganglia ischemic stroke (LBGIS), 44 patients with right basal ganglia ischemic stroke (RBGIS), and 41 individuals in a healthy control (HC) group. Rs-fMRI studies were performed on these patients, and the pre-processed rs-fMRI data were analyzed using VMHC method. Subsequently, the VMHC values were compared between three groups using a one-way ANOVA and post hoc analysis. Correlation analysis with clinical scales was also conducted.

RESULTS: The results indicated that compared to the HC group, significant differences were detected in postcentral gyrus, extending to precentral gyrus in both BGIS groups. Post hoc analysis showed that in the pairwise ROI-based comparison, individuals with LBGIS and RBGIS exhibited reduced VMHC values compared to HC groups. There was no significant difference between the LBGIS and RBGIS groups. In the LBGIS group, the VMHC value showed a negative correlation with NIHSS and a positive correlation with BI.

CONCLUSION: The analysis of VMHC in rs-fMRI revealed a pattern of brain functional remodeling in patients with unilateral BGIS, marked by reduced synchronization and coordination between hemispheres. This may contribute to the understanding of the neurological mechanisms underlying motor dysfunction in these patients.

PMID:39385827 | PMC:PMC11461242 | DOI:10.3389/fnagi.2024.1408685

J Affect Disord. 2024 Oct 7:S0165-0327(24)01668-9. doi: 10.1016/j.jad.2024.10.009. Online ahead of print.

ABSTRACT

BACKGROUND: Investigating how the interaction between the orbitofrontal cortex (OFC) and various brain regions/functional networks in major depressive disorder (MDD) patients with a history of suicide attempt (SA) holds importance for understanding the neurobiology of this population.

METHODS: We employed resting-state functional magnetic resonance imaging (rs-fMRI) to analyze the OFC's functional segregation in 586 healthy individuals. A network analysis framework was then applied to rs-fMRI data from 86 MDD-SA patients and 85 MDD-Control patients, utilizing seed mappings of OFC subregions and a multi-connectivity-indicator strategy involving cross-correlation, total interdependencies, Granger causality, and machine learning.

RESULTS: Four functional subregions of left and right OFC, were designated as seed regions of interest. Relative to the MDD-Control group, the MDD-SA group exhibited enhanced functional connectivity (FC) and attenuated interaction between the OFC and the sensorimotor network, imbalanced communication between the OFC and the default mode network, enhanced FC and interaction between the OFC and the ventral attention network, enhanced interaction between the OFC and the salience network, and attenuated FC between the OFC and the frontoparietal network.

LIMITATIONS: The medication and treatment condition of patients with MDD was not controlled, so the medication effect on the alteration model cannot be affirmed.

CONCLUSION: The findings suggest an imbalanced interaction pattern between the OFC subregions and a set of cognition- and emotion-related functional networks/regions in the MDD-SA group.

PMID:39383951 | DOI:10.1016/j.jad.2024.10.009

Brain Cogn. 2024 Oct 8;181:106223. doi: 10.1016/j.bandc.2024.106223. Online ahead of print.

ABSTRACT

INTRODUCTION: This study aims to explore the impact of smoking on intrinsic brain activity among high-altitude (HA) populations. Smoking is associated with various neural alterations, but it remains unclear whether smokers in HA environments exhibit specific neural characteristics.

METHODS: We employed ALFF and fALFF methods across different frequency bands to investigate differences in brain functional activity between high-altitude smokers and non-smokers. 31 smokers and 31 non-smokers from HA regions participated, undergoing resting-state functional magnetic resonance imaging (rs-fMRI) scans. ALFF/fALFF values were compared between the two groups. Correlation analyses explored relationships between brain activity and clinical data.

RESULTS: Smokers showed increased ALFF values in the right superior frontal gyrus (R-SFG), right middle frontal gyrus (R-MFG), right anterior cingulate cortex (R-ACC), right inferior frontal gyrus (R-IFG), right superior/medial frontal gyrus (R-MSFG), and left SFG compared to non-smokers in HA. In sub-frequency bands (0.01-0.027 Hz and 0.027-0.073 Hz), smokers showed increased ALFF values in R-SFG, R-MFG, right middle cingulate cortex (R-MCC), R-MSFG, Right precentral gyrus and L-SFG while decreased fALFF values were noted in the right postcentral and precentral gyrus in the 0.01-0.027 Hz band. Negative correlations were found between ALFF values in the R-SFG and smoking years.

CONCLUSION: Our study reveals the neural characteristics of smokers in high-altitude environments, highlighting the potential impact of smoking on brain function. These results provide new insights into the neural mechanisms of high-altitude smoking addiction and may inform the development of relevant intervention measures.

PMID:39383675 | DOI:10.1016/j.bandc.2024.106223

Front Hum Neurosci. 2024 Sep 24;18:1461590. doi: 10.3389/fnhum.2024.1461590. eCollection 2024.

ABSTRACT

Parcellations of resting-state functional magnetic resonance imaging (rs-fMRI) data are widely used to create topographical maps of functional networks in the human brain. While such network maps are highly useful for studying brain organization and function, they usually require large sample sizes to make them, thus creating practical limitations for researchers that would like to carry out parcellations on data collected in their labs. Furthermore, it can be difficult to quantitatively evaluate the results of a parcellation since networks are usually identified using a clustering algorithm, like principal components analysis, on the results of a single group-averaged connectivity map. To address these challenges, we developed the FunMaps method: a parcellation routine that intrinsically incorporates stability and replicability of the parcellation by keeping only network distinctions that agree across halves of the data over multiple random iterations. Here, we demonstrate the efficacy and flexibility of FunMaps, while describing step-by-step instructions for running the program. The FunMaps method is publicly available on GitHub (https://github.com/persichetti-lab/FunMaps). It includes source code for running the parcellation and auxiliary code for preparing data, evaluating the parcellation, and displaying the results.

PMID:39381142 | PMC:PMC11458417 | DOI:10.3389/fnhum.2024.1461590

Front Neurol. 2024 Sep 24;15:1439939. doi: 10.3389/fneur.2024.1439939. eCollection 2024.

ABSTRACT

BACKGROUND: Resting-state functional magnetic resonance imaging (rs-fMRI) reveals diverse neural activity patterns in cervical spondylosis (CS) patients. However, the reported results are inconsistent. Therefore, our objective was to conduct a meta-analysis to synthesize the findings from existing rs-fMRI studies and identify consistent patterns of neural brain activity alterations in patients with CS.

MATERIALS AND METHODS: A systematic search was conducted across PubMed, Web of Knowledge, Embase, Google Scholar, and CNKI for rs-fMRI studies that compared CS patients with healthy controls (HCs), up to January 28, 2024. Significant cluster coordinates were extracted for comprehensive analysis.

RESULTS: We included 16 studies involving 554 CS patients and 488 HCs. CS patients demonstrated decreased brain function in the right superior temporal gyrus and left postcentral gyrus, and increased function in the left superior frontal gyrus. Jackknife sensitivity analysis validated the robustness of these findings, and Egger's test confirmed the absence of significant publication bias (p > 0.05). Meta-regression showed no significant impact of age or disease duration differences on the results.

CONCLUSION: This meta-analysis confirms consistent alterations in specific brain regions in CS patients, highlighting the potential of rs-fMRI to refine diagnostic and therapeutic strategies.

SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/prospero/, identifier CRD42024496263.

PMID:39381074 | PMC:PMC11460301 | DOI:10.3389/fneur.2024.1439939

Neuroimage. 2024 Oct 6;301:120884. doi: 10.1016/j.neuroimage.2024.120884. Online ahead of print.

ABSTRACT

Resting-state of the human brain has been described by a combination of various basis modes including the default mode network (DMN) identified by fMRI BOLD signals in human brains. Whether DMN is the most dominant representation of the resting-state has been under question. Here, we investigated the unexplored yet fundamental nature of the resting-state. In the absence of global signal regression for the analysis of brain-wide spatial activity pattern, the fMRI BOLD spatiotemporal signals during the rest were completely decomposed into time-invariant spatial-expression basis modes (SEBMs) and their time-evolution basis modes (TEBMs). Contrary to our conventional concept above, similarity clustering analysis of the SEBMs from 166 human brains revealed that the most dominant SEBM cluster is an asymmetric mode where the distribution of the sign of the components is skewed in one direction, for which we call essential mode (EM), whereas the second dominant SEBM cluster resembles the spatial pattern of DMN. Having removed the strong 1/f noise in the power spectrum of TEBMs, the genuine oscillatory behavior embedded in TEBMs of EM and DMN-like mode was uncovered around the low-frequency range below 0.2 Hz.

PMID:39378912 | DOI:10.1016/j.neuroimage.2024.120884

PLoS Biol. 2024 Oct 8;22(10):e3002797. doi: 10.1371/journal.pbio.3002797. eCollection 2024 Oct.

ABSTRACT

Our behavior and mental states are constantly shaped by our environment and experiences. However, little is known about the response of brain functional connectivity to environmental, physiological, and behavioral changes on different timescales, from days to months. This gives rise to an urgent need for longitudinal studies that collect high-frequency data. To this end, for a single subject, we collected 133 days of behavioral data with smartphones and wearables and performed 30 functional magnetic resonance imaging (fMRI) scans measuring attention, memory, resting state, and the effects of naturalistic stimuli. We find traces of past behavior and physiology in brain connectivity that extend up as far as 15 days. While sleep and physical activity relate to brain connectivity during cognitively demanding tasks, heart rate variability and respiration rate are more relevant for resting-state connectivity and movie-watching. This unique data set is openly accessible, offering an exceptional opportunity for further discoveries. Our results demonstrate that we should not study brain connectivity in isolation, but rather acknowledge its interdependence with the dynamics of the environment, changes in lifestyle, and short-term fluctuations such as transient illnesses or restless sleep. These results reflect a prolonged and sustained relationship between external factors and neural processes. Overall, precision mapping designs such as the one employed here can help to better understand intraindividual variability, which may explain some of the observed heterogeneity in fMRI findings. The integration of brain connectivity, physiology data and environmental cues will propel future environmental neuroscience research and support precision healthcare.

PMID:39378200 | PMC:PMC11460715 | DOI:10.1371/journal.pbio.3002797