Most recent paper

Biomed Rep. 2024 May 22;21(1):104. doi: 10.3892/br.2024.1792. eCollection 2024 Jul.

ABSTRACT

Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis results in severe neuropsychiatric symptoms and persistent cognitive impairment; however, the underlying mechanism is still not fully understood. The present study utilized the degree centrality (DC), functional connectivity (FC) and multivariate pattern analysis (MVPA) to further explore neurofunctional symptoms in patients with anti-NMDAR encephalitis. A total of 29 patients with anti-NMDAR encephalitis and 26 healthy controls (HCs) were enrolled for neuropsychological assessment and resting-state functional MRI (rs-fMRI) scans. DC, FC and MVPA were examined to investigate cerebral functional activity and distinguish neuroimaging characteristics between the patient and HC groups based on the rs-fMRI data. Compared with the HCs, the patients exhibited cognitive deficits, anxiety and depression. In the DC analysis, the patients exhibited significantly decreased DC strength in the left rectus gyrus, left caudate nucleus (LCN) and bilateral superior medial frontal gyrus, as well as increased DC strength in the cerebellar anterior lobe, compared with the HCs. In the subsequent FC analysis, the LCN showed decreased FC strength in the bilateral middle frontal gyrus and right precuneus. Furthermore, correlation analysis indicated that disrupted cerebral functional activity was significantly correlated with the alerting effect and Hamilton Depression Scale score. Using DC maps and receiver operating characteristic curve analysis, the MVPA classifier exhibited an area under curve of 0.79, and the accuracy classification rate was 76.36%, with a sensitivity of 79.31% and a specificity of 78.18%. The present study revealed that the disrupted functional activity of hub and related networks in the cerebellum, including the default mode network and executive control network, contributed to deficits in cognition and emotion in patients with anti-NMDAR encephalitis. In conclusion, the present study provided imaging evidence and primary diagnostic markers for pathological and compensatory mechanisms of anti-NMDAR encephalitis, with the aim of improving the understanding of this disease.

PMID:38827495 | PMC:PMC11140295 | DOI:10.3892/br.2024.1792

Front Psychiatry. 2024 May 16;15:1353103. doi: 10.3389/fpsyt.2024.1353103. eCollection 2024.

ABSTRACT

BACKGROUND: Insular subdivisions show distinct patterns of resting state functional connectivity with specific brain regions, each with different functional significance in chronic cigarette smokers. This study aimed to explore the altered dynamic functional connectivity (dFC) of distinct insular subdivisions in smokers.

METHODS: Resting-state BOLD data of 31 smokers with nicotine dependence and 27 age-matched non-smokers were collected. Three bilateral insular regions of interest (dorsal, ventral, and posterior) were set as seeds for analyses. Sliding windows method was used to acquire the dFC metrics of different insular seeds. Support vector machine based on abnormal insular dFC was applied to classify smokers from non-smokers.

RESULTS: We found that smokers showed lower dFC variance between the left ventral anterior insula and both the right superior parietal cortex and the left inferior parietal cortex, as well as greater dFC variance the right ventral anterior insula with the right middle cingulum cortex relative to non-smokers. Moreover, compared to non-smokers, it is found that smokers demonstrated altered dFC variance of the right dorsal insula and the right middle temporal gyrus. Correlation analysis showed the higher dFC between the right dorsal insula and the right middle temporal gyrus was associated with longer years of smoking. The altered insular subdivision dFC can classify smokers from non-smokers with an accuracy of 89.66%, a sensitivity of 96.30% and a specify of 83.87%.

CONCLUSIONS: Our findings highlighted the abnormal patterns of fluctuating connectivity of insular subdivision circuits in smokers and suggested that these abnormalities may play a significant role in the mechanisms underlying nicotine addiction and could potentially serve as a neural biomarker for addiction treatment.

PMID:38827448 | PMC:PMC11140567 | DOI:10.3389/fpsyt.2024.1353103

medRxiv [Preprint]. 2024 May 25:2024.05.24.24307883. doi: 10.1101/2024.05.24.24307883.

ABSTRACT

The brain's default mode network (DMN) plays a role in social cognition, with altered DMN function being associated with social impairments across various neuropsychiatric disorders. In the present study, we examined the genetic relationship between sociability and DMN-related resting-state functional magnetic resonance imaging (rs-fMRI) traits. To this end, we used genome-wide association summary statistics for sociability and 31 activity and 64 connectivity DMN-related rs-fMRI traits (N=34,691-342,461). First, we examined global and local genetic correlations between sociability and the rs-fMRI traits. Second, to assess putatively causal relationships between the traits, we conducted bi-directional Mendelian randomisation (MR) analyses. Finally, we prioritised genes influencing both sociability and rs-fMRI traits by combining three methods: gene-expression eQTL MR analyses, the CELLECT framework using single-nucleus RNA-seq data, and network propagation in the context of a protein-protein interaction network. Significant local genetic correlations were found between sociability and two rs-fMRI traits, one representing spontaneous activity within the temporal cortex, the other representing connectivity between the frontal/cingulate and angular/temporal cortices. Sociability affected 12 rs-fMRI traits when allowing for weakly correlated genetic instruments. Combing all three methods for gene prioritisation, we defined 17 highly prioritised genes, with DRD2 and LINGO1 showing the most robust evidence across all analyses. By integrating genetic and transcriptomics data, our gene prioritisation strategy may serve as a blueprint for future studies. The prioritised genes could be explored as potential biomarkers for social dysfunction in the context of neuropsychiatric disorders and as drug target genes.

PMID:38826220 | PMC:PMC11142265 | DOI:10.1101/2024.05.24.24307883

bioRxiv [Preprint]. 2024 May 23:2024.05.23.595327. doi: 10.1101/2024.05.23.595327.

ABSTRACT

Whole-brain intrinsic activity as detected by resting-state fMRI can be summarized by three primary spatiotemporal patterns. These patterns have been shown to change with different brain states, especially arousal. The noradrenergic locus coeruleus (LC) is a key node in arousal circuits and has extensive projections throughout the brain, giving it neuromodulatory influence over the coordinated activity of structurally separated regions. In this study, we used optogenetic-fMRI in rats to investigate the impact of LC stimulation on the global signal and three primary spatiotemporal patterns. We report small, spatially specific changes in global signal distribution as a result of tonic LC stimulation, as well as regional changes in spatiotemporal patterns of activity at 5 Hz tonic and 15 Hz phasic stimulation. We also found that LC stimulation had little to no effect on the spatiotemporal patterns detected by complex principal component analysis. These results show that the effects of LC activity on the BOLD signal in rats may be small and regionally concentrated, as opposed to widespread and globally acting.

PMID:38826205 | PMC:PMC11142206 | DOI:10.1101/2024.05.23.595327

Neuroimage Clin. 2024 May 24;43:103621. doi: 10.1016/j.nicl.2024.103621. Online ahead of print.

ABSTRACT

Greater physical activity and better sleep are associated with reduced risk of cognitive decline and dementia among older adults, but little is known about their combined associations with measures of brain function and neuropathology. This study investigated potential independent and interactive cross-sectional relationships between actigraphy-estimated total volume of physical activity (TVPA) and sleep patterns [i.e., total sleep time (TST), sleep efficiency (SE)] with resting-state functional magnetic resonance imaging (rs-fMRI) measures of large scale network connectivity and positron emission tomography (PET) measures of amyloid-β. Participants were 135 non-demented older adults from the BIOCARD study (116 cognitively normal and 19 with mild cognitive impairment; mean age = 70.0 years). Using multiple linear regression analyses, we assessed the association between TVPA, TST, and SE with connectivity within the default-mode, salience, and fronto-parietal control networks, and with network modularity, a measure of network segregation. Higher TVPA and SE were independently associated with greater network modularity, although the positive relationship of SE with modularity was only present in amyloid-negative individuals. Additionally, higher TVPA was associated with greater connectivity within the default-mode network, while greater SE was related to greater connectivity within the salience network. In contrast, longer TST was associated with lower network modularity, particularly among amyloid-positive individuals, suggesting a relationship between longer sleep duration and greater network disorganization. Physical activity and sleep measures were not associated with amyloid positivity. These data suggest that greater physical activity levels and more efficient sleep may promote more segregated and potentially resilient functional networks and increase functional connectivity within specific large-scale networks and that the relationship between sleep and functional networks connectivity may depend on amyloid status.

PMID:38823249 | DOI:10.1016/j.nicl.2024.103621

Asian J Psychiatr. 2024 May 19;97:104093. doi: 10.1016/j.ajp.2024.104093. Online ahead of print.

ABSTRACT

BACKGROUND: Childhood maltreatment (CM) is a well-established risk factor for major depressive disorder (MDD). The neural mechanisms linking childhood maltreatment experiences to changes in brain functional networks and the onset of depression are not fully understood.

METHODS: In this study, we enrolled 66 patients with MDD and 31 healthy controls who underwent resting-state fMRI scans and neuropsychological assessments. We employed multivariate linear regression to examine the neural associations of CM and depression, specifically focusing on the bilateral occipital functional connectivity (OFC) networks relevant to MDD. Subsequently, a two-step mediation analysis was conducted to assess whether the OFC network mediated the relationship between CM experiences and the severity of depression.

RESULTS: Our study showed that patients with MDD exhibited reduced OFC strength, particularly in the occipito-temporal, parietal, and premotor regions. These reductions were negatively correlated with CM scores and the severity of depression. Notably, the overlapping regions in the bilateral OFC networks, affected by both CM experiences and depressive severity, were primarily observed in the bilateral cuneus, left angular and calcarine, as well as the right middle frontal cortex and superior parietal cortex. Furthermore, the altered strengths of the OFC networks were identified as positive mediators of the impact of CM history on depression symptoms in patients with MDD.

CONCLUSION: We have demonstrated that early exposure to CM may increase vulnerability to depression by influencing the brain's network. These findings provide new insights into understanding the pathological mechanism underlying depressive symptoms induced by CM.

PMID:38823080 | DOI:10.1016/j.ajp.2024.104093

Neuropsychopharmacology. 2024 May 31. doi: 10.1038/s41386-024-01884-5. Online ahead of print.

ABSTRACT

TSPO ligands are promising alternatives to benzodiazepines in the treatment of anxiety, as they display less pronounced side effects such as sedation, cognitive impairment, tolerance development and abuse potential. In a randomized double-blind repeated-measures study we compare a benzodiazepine (alprazolam) to a TSPO ligand (etifoxine) by assessing side effects and acquiring resting-state fMRI data from 34 healthy participants after 5 days of taking alprazolam, etifoxine or a placebo. To study the effects of the pharmacological interventions in fMRI in detail and across different scales, we combine in our study complementary analysis strategies related to whole-brain functional network connectivity, local connectivity analysis expressed in regional homogeneity, fluctuations in low-frequency BOLD amplitudes and coherency of independent resting-state networks. Participants reported considerable adverse effects such as fatigue, sleepiness and concentration impairments, related to the administration of alprazolam compared to placebo. In resting-state fMRI we found a significant decrease in functional connection density, network efficiency and a decrease in the networks rich-club coefficient related to alprazolam. While observing a general decrease in regional homogeneity in high-level brain networks in the alprazolam condition, we simultaneously could detect an increase in regional homogeneity and resting-state network coherence in low-level sensory regions. Further we found a general increase in the low-frequency compartment of the BOLD signal. In the etifoxine condition, participants did not report any significant side effects compared to the placebo, and we did not observe any corresponding modulations in our fMRI metrics. Our results are consistent with the idea that sedation globally disconnects low-level functional networks, but simultaneously increases their within-connectivity. Further, our results point towards the potential of TSPO ligands in the treatment of anxiety and depression.

PMID:38822128 | DOI:10.1038/s41386-024-01884-5

Nat Commun. 2024 May 31;15(1):4669. doi: 10.1038/s41467-024-49140-0.

ABSTRACT

Measures of fMRI resting-state functional connectivity (rs-FC) are an essential tool for basic and clinical investigations of fronto-limbic circuits. Understanding the relationship between rs-FC and the underlying patterns of neural activity in these circuits is therefore vital. Here we introduced inhibitory designer receptors exclusively activated by designer drugs (DREADDs) into the amygdala of two male macaques. We evaluated the causal effect of activating the DREADD receptors on rs-FC and neural activity within circuits connecting amygdala and frontal cortex. Activating the inhibitory DREADD increased rs-FC between amygdala and ventrolateral prefrontal cortex. Neurophysiological recordings revealed that the DREADD-induced increase in fMRI rs-FC was associated with increased local field potential coherency in the alpha band (6.5-14.5 Hz) between amygdala and ventrolateral prefrontal cortex. Thus, our multi-modal approach reveals the specific signature of neuronal activity that underlies rs-FC in fronto-limbic circuits.

PMID:38821963 | DOI:10.1038/s41467-024-49140-0

J Affect Disord. 2024 May 29:S0165-0327(24)00885-1. doi: 10.1016/j.jad.2024.05.141. Online ahead of print.

ABSTRACT

Personalized functional connectivity mapping has been demonstrated to be promising in identifying underlying neurophysiological basis for brain disorders and treatment effects. Electroconvulsive therapy (ECT) has been proved to be an effective treatment for major depressive disorder (MDD) while its active mechanisms remain unclear. Here, 46 MDD patients before and after ECT as well as 46 demographically matched healthy controls (HC) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans. A spatially regularized form of non-negative matrix factorization (NMF) was used to accurately identify functional networks (FNs) in individuals to map individual-level static and dynamic functional network connectivity (FNC) to reveal the underlying neurophysiological basis of therepetical effects of ECT for MDD. Moreover, these static and dynamic FNCs were used as features to predict the clinical treatment outcomes for MDD patients. We found that ECT could modulate both static and dynamic large-scale FNCs at individual level in MDD patients, and dynamic FNCs were closely associated with depression and anxiety symptoms. Importantly, we found that individual FNCs, particularly the individual dynamic FNCs could better predict the treatment outcomes of ECT suggesting that dynamic functional connectivity analysis may be better to link brain functional characteristics with clinical symptoms and treatment outcomes. Taken together, our findings provide new evidence for the active mechanisms and biomarkers for ECT to improve diagnostic accuracy and to guide individual treatment selection for MDD patients.

PMID:38821362 | DOI:10.1016/j.jad.2024.05.141

J Neuroradiol. 2024 May 29:101209. doi: 10.1016/j.neurad.2024.101209. Online ahead of print.

ABSTRACT

BACKGROUND: It remains unclear whether alterations in brain function occur in the early stage of pediatric type 1 diabetes mellitus(T1DM). We aimed to examine changes in spontaneous brain activity and functional connectivity (FC) in children with T1DM using resting-state functional magnetic resonance imaging (rs-fMRI), and to pinpoint potential links between neural changes and cognitive performance.

METHODS: In this study, 22 T1DM children and 21 age-, sex-matched healthy controls underwent rs-fMRI. The amplitude of low frequency fluctuations (ALFF) and seed-based FC analysis were performed to examine changes in intrinsic brain activity and functional networks in T1DM children. Partial correlation analyses were utilized to explore the correlations between ALFF values and clinical parameters.

RESULTS: The ALFF values were significantly lower in the lingual gyrus (LG) and higher in the left medial superior frontal gyrus (MSFG) in T1DM children compared to controls. Subsequent FC analysis indicated that the LG had decreased FC with bilateral inferior occipital gyrus, and the left MSFG had decreased FC with right precentral gyrus, right inferior parietal gyrus and right postcentral gyrus in children with T1DM. The ALFF values of LG were positively correlated with full-scale intelligence quotient and age at disease onset in T1DM children, while the ALFF values of left MSFG were positively correlated with working memory scores.

CONCLUSION: Our findings revealed abnormal spontaneous activity and FC in brain regions related to visual, memory, default mode network, and sensorimotor network in the early stage of T1DM children, which may aid in further understanding the mechanisms underlying T1DM-associated cognitive dysfunction.

PMID:38821316 | DOI:10.1016/j.neurad.2024.101209

Psychol Sport Exerc. 2024 May 29:102678. doi: 10.1016/j.psychsport.2024.102678. Online ahead of print.

ABSTRACT

INTRODUCTION: Long-term motor skill training has been shown to induce anatomical and functional neuroplasticity. World class gymnasts (WCGs) provide a unique opportunity to investigate the effect of long-term intensive training on neuroplasticity. Previous resting-state fMRI studies have demonstrated a high efficient information processing related to motor and cognitive functions in gymnasts compared with the healthy controls (HCs). However, most research treated brain signals as static, overlooking the fact that the brain is a complex and dynamic system. In this study, we employed functional stability, a new metric based on dynamic functional connectivity (FC), to examine the impact of long-term intensive training on the functional architecture in the WCGs.

METHODS: We first conducted a voxel-wise analysis of functional stability between the WCGs and HCs. Then, we applied FC density (FCD) to explore whether regions with modified functional stability were also accompanied by changes in connection patterns in the WCGs. We identified overlapping regions showing significant differences in both functional stability and FCD. Finally, we applied seed-based correlation analysis (SCA) to determine the detailed changes in connection patterns between the WCGs and HCs within these overlapping regions.

RESULTS: Compared with the HCs, the WCGs exhibited higher functional stability in the bilateral angular gyrus (AG), bilateral inferior temporal gyrus (ITG), bilateral precentral gyrus, and right superior frontal gyrus and lower functional stability in the bilateral hippocampus, bilateral caudate, right rolandic operculum, left superior temporal gyrus, right middle frontal gyrus, right middle cingular cortex, and right precuneus. We found that the bilateral AG and ITG not only showed higher functional stability but also increased global and long-range FCD in the WCGs relative to the HCs. The right precuneus displayed lower functional stability as well as decreased local, long-range, and global FCD in the WCGs. Both AG and ITG showed higher FC with regions in the default mode network (DMN) in the WCGs than in the HCs.

CONCLUSIONS: The increased functional stability in the AG and ITG might be associated with enhanced functional integration within the DMN in the WCGs. These findings may offer new spatiotemporal evidence for the impact of long-term intensive training on neuroplasticity.

PMID:38821251 | DOI:10.1016/j.psychsport.2024.102678

Schizophr Bull. 2024 May 31:sbae084. doi: 10.1093/schbul/sbae084. Online ahead of print.

ABSTRACT

BACKGROUND: Schizophrenia, a multifaceted psychiatric disorder characterized by functional dysconnectivity, poses significant challenges in clinical practice. This study explores the potential of functional connectivity (FC)-based searchlight multivariate pattern analysis (CBS-MVPA) to discriminate between schizophrenia patients and healthy controls while also predicting clinical variables.

STUDY DESIGN: We enrolled 112 schizophrenia patients and 119 demographically matched healthy controls. Resting-state functional magnetic resonance imaging data were collected, and whole-brain FC subnetworks were constructed. Additionally, clinical assessments and cognitive evaluations yielded a dataset comprising 36 clinical variables. Finally, CBS-MVPA was utilized to identify subnetworks capable of effectively distinguishing between the patient and control groups and predicting clinical scores.

STUDY RESULTS: The CBS-MVPA approach identified 63 brain subnetworks exhibiting significantly high classification accuracies, ranging from 62.2% to 75.6%, in distinguishing individuals with schizophrenia from healthy controls. Among them, 5 specific subnetworks centered on the dorsolateral superior frontal gyrus, orbital part of inferior frontal gyrus, superior occipital gyrus, hippocampus, and parahippocampal gyrus showed predictive capabilities for clinical variables within the schizophrenia cohort.

CONCLUSION: This study highlights the potential of CBS-MVPA as a valuable tool for localizing the information related to schizophrenia in terms of brain network abnormalities and capturing the relationship between these abnormalities and clinical variables, and thus, deepens our understanding of the neurological mechanisms of schizophrenia.

PMID:38819252 | DOI:10.1093/schbul/sbae084

Brain Res. 2024 May 28:149023. doi: 10.1016/j.brainres.2024.149023. Online ahead of print.

ABSTRACT

INTRODUCTION: Stroke recovery is a complex process influenced by various factors, including specific neural reorganization. The objective of this systematic review was to identify important functional connectivity (FC) changes in resting-state fMRI data that were often correlated with motor, emotional, and cognitive outcome improvement.

METHOD: A systematic search using PubMed and SCOPUS databases was conducted to identify relevant studies published between 2010 and 2023.

RESULTS: A total of 766 studies were identified, of which 20 studies (602 S individuals) met the inclusion criteria. Fourteen studies focussed on motor recovery while six on cognitive recovery. All studies reported interhemispheric FC to be strongly associated with motor and cognitive recovery. The preservation and changes of M1-M1 (eight incidences) and M1-SMA (nine incidences) FC were found to be strongly correlated with motor function improvement. For cognitive recovery, restoration and preservation of FC with and between default mode network (DMN)-related regions were important for the process.

CONCLUSIONS: This review identified specific patterns of FC that were consistently reported with recovery of motor and cognitive function. The findings may serve in refining future management strategies to enhance patient outcomes.

PMID:38815644 | DOI:10.1016/j.brainres.2024.149023

Neuroimage Clin. 2024 May 25;43:103622. doi: 10.1016/j.nicl.2024.103622. Online ahead of print.

ABSTRACT

We previously reported that people with multiple sclerosis (pwMS) who have been using cannabis frequently over many years can have significant cognitive improvements accompanied by concomitant task-specific changes in brain activation following 28 days of cannabis abstinence. We now hypothesize that the default Mode Network (DMN), known to modulate cognition, would also show an improved pattern of activation align with cognitive improvement following 28 days of drug abstinence. Thirty three cognitively impaired pwMS who were frequent cannabis users underwent a neuropsychological assessment and fMRI at baseline. Individuals were then assigned to a cannabis continuation (CC, n = 15) or withdrawal (CW, n = 18) group and the cognitive and imaging assessments were repeated after 28 days. Compliance with cannabis withdrawal was checked with regular urine monitoring. Following acquisition of resting state fMRI (rs-fMRI), data were processed using independent component analysis (ICA) to identify the DMN spatial map. Between and within group analyses were carried out using dual regression for voxel-wise comparisons of the DMN. Clusters of voxels were considered statistically significant if they survived threshold-free cluster enhancement (TFCE) correction at p < 0.05. The two groups were well matched demographically and neurologically at baseline. The dual regression analysis revealed no between group differences at baseline in the DMN. By day 28, the CW group in comparison to the CC group had increased activation in the left posterior cingulate, and right, angular gyrus (p < 0.05 for both, TFCE). A within group analysis for the CC group revealed no changes in resting state (RS) networks. Within group analysis of the CW group revealed increased activation at day 28 versus baseline in the left posterior cingulate, right angular gyrus, left hippocampus (BA 36), and the right medial prefrontal cortex (p < 0.05). The CW group showed significant improvements in multiple cognitive domains. In summary, our study revealed that abstaining from cannabis for 28 days reverses activation of DMN activity in pwMS in association with improved cognition across several domains.

PMID:38815510 | DOI:10.1016/j.nicl.2024.103622

PLoS One. 2024 May 30;19(5):e0300720. doi: 10.1371/journal.pone.0300720. eCollection 2024.

ABSTRACT

Alterations in the brain's connectivity or the interactions among brain regions have been studied with the aid of resting state (rs)fMRI data attained from large numbers of healthy subjects of various demographics. This has been instrumental in providing insight into how a phenotype as fundamental as age affects the brain. Although machine learning (ML) techniques have already been deployed in such studies, novel questions are investigated in this work. We study whether young brains develop properties that progressively resemble those of aged brains, and if the aging dynamics of older brains provide information about the aging trajectory in young subjects. The degree of a prospective monotonic relationship will be quantified, and hypotheses of brain aging trajectories will be tested via ML. Furthermore, the degree of functional connectivity across the age spectrum of three datasets will be compared at a population level and across sexes. The findings scrutinize similarities and differences among the male and female subjects at greater detail than previously performed.

PMID:38814972 | DOI:10.1371/journal.pone.0300720

Proc Natl Acad Sci U S A. 2024 Jun 4;121(23):e2318641121. doi: 10.1073/pnas.2318641121. Epub 2024 May 30.

ABSTRACT

A balanced excitation-inhibition ratio (E/I ratio) is critical for healthy brain function. Normative development of cortex-wide E/I ratio remains unknown. Here, we noninvasively estimate a putative marker of whole-cortex E/I ratio by fitting a large-scale biophysically plausible circuit model to resting-state functional MRI (fMRI) data. We first confirm that our model generates realistic brain dynamics in the Human Connectome Project. Next, we show that the estimated E/I ratio marker is sensitive to the gamma-aminobutyric acid (GABA) agonist benzodiazepine alprazolam during fMRI. Alprazolam-induced E/I changes are spatially consistent with positron emission tomography measurement of benzodiazepine receptor density. We then investigate the relationship between the E/I ratio marker and neurodevelopment. We find that the E/I ratio marker declines heterogeneously across the cerebral cortex during youth, with the greatest reduction occurring in sensorimotor systems relative to association systems. Importantly, among children with the same chronological age, a lower E/I ratio marker (especially in the association cortex) is linked to better cognitive performance. This result is replicated across North American (8.2 to 23.0 y old) and Asian (7.2 to 7.9 y old) cohorts, suggesting that a more mature E/I ratio indexes improved cognition during normative development. Overall, our findings open the door to studying how disrupted E/I trajectories may lead to cognitive dysfunction in psychopathology that emerges during youth.

PMID:38814872 | DOI:10.1073/pnas.2318641121

Brain Connect. 2024 May 30. doi: 10.1089/brain.2023.0080. Online ahead of print.

ABSTRACT

BACKGROUND: Functional Magnetic Resonance Imaging (fMRI) has the potential to provide non-invasive functional mapping of the brain with high spatial and temporal resolution. However, fMRI independent components (ICs) must be manually inspected, selected, and interpreted, requiring time and expertise. We propose a novel approach for automated labeling of fMRI independent components by establishing their characteristic spatio-functional relationship.

METHODS: The approach identifies 9 Resting State Networks and 45 independent components and generates a functional activation feature map that quantifies the spatial distribution, relative to an anatomical labeled atlas, of the z-scores of each IC across a cohort of 176 subjects. The cosine-similarity metric was used to classify unlabeled independent component based on the similarity to the spatial distribution of activation with the pre-generated feature map. The approach was tested on three fMRI datasets from the 1000 functional connectome project, consisting of 280 subjects, that were not included in feature map generation.

RESULTS: The results demonstrate the effectiveness of the approach in classifying independent components based on their spatial features with an accuracy of better than 95%.

CONCLUSIONS: The approach significantly reduces expert time and computation time required for labeling independent components while improving reliability and accuracy. The spatio-functional relationship also provides an explainable relationship between the functional activation and the anatomically defined regions.

PMID:38814830 | DOI:10.1089/brain.2023.0080

Brain Imaging Behav. 2024 May 30. doi: 10.1007/s11682-024-00896-5. Online ahead of print.

ABSTRACT

Since depression is common in amyotrophic lateral sclerosis (ALS) patients, we aimed to explore the specific brain functional network dynamics in ALS patients with depression (ALS-D) compared with healthy controls (HCs) and ALS patients without depressive symptoms (ALS-ND). According to the DSM-V, 32 ALS-D patients were selected from a large and newly diagnosed ALS cohort. Then, 32 demographic- and cognitive-matched ALS-ND patients were also selected, and 64 HCs were recruited. These participants underwent resting-state fMRI scans, and functional connectivity state analysis and dynamic graph theory were applied to evaluate brain functional network dynamics. Moreover, the Hamilton Depression Rating Scale (HDRS) was used to quantify depressive symptoms in the ALS-D patients. Four distinct states were identified in the ALS-D patients and controls. Compared with that in HCs, the fraction rate (FR) in state 2 was significantly decreased in ALS-D patients, and the FR in state 4 was significantly increased in ALS-D patients. Compared with that of HCs, the dwell time in state 4 was significantly increased in the ALS-D patients. Moreover, compared with that in the ALS-D patients, the FR in state 3 was significantly decreased in the ALS-ND patients. Among the ALS-D patients, there was the suggestion of a positive association between HDRS scores and dwell time of state 4, but this association did not reach statistical significance (r = 0.354; p = 0.055). Depression is an important feature of ALS patients, and we found a special pattern of brain functional network dynamics in ALS-D patients. Our findings may play an important role in understanding the mechanism underlying depression in ALS patients and help develop therapeutic interventions for depressed ALS patients.

PMID:38814545 | DOI:10.1007/s11682-024-00896-5

Eur Child Adolesc Psychiatry. 2024 May 30. doi: 10.1007/s00787-024-02474-y. Online ahead of print.

ABSTRACT

Recent studies on autism spectrum disorder (ASD) have identified recurring states dominated by similar coactivation pattern (CAP) and revealed associations between dysfunction in seed-based large-scale brain networks and clinical symptoms. However, the presence of abnormalities in moment-to-moment whole-brain dynamics in ASD remains uncertain. In this study, we employed seed-free CAP analysis to identify transient brain activity configurations and investigate dynamic abnormalities in ASD. We utilized a substantial multisite resting-state fMRI dataset consisting of 354 individuals with ASD and 446 healthy controls (HCs, from HC groups and 2). CAP were generated from a subgroup of all HC subjects (HC group 1) through temporal K-means clustering, identifying four CAPs. These four CAPs exhibited either the activation or inhibition of the default mode network (DMN) and were grouped into two pairs with opposing spatial CAPs. CAPs for HC group 2 and ASD were identified by their spatial similarity to those for HC group 1. Compared with individuals in HC group 2, those with ASD spent more time in CAPs involving the ventral attention network but less time in CAPs related to executive control and the dorsal attention network. Support vector machine analysis demonstrated that the aberrant dynamic characteristics of CAPs achieved an accuracy of 74.87% in multisite classification. In addition, we used whole-brain dynamics to predict symptom severity in ASD. Our findings revealed whole-brain dynamic functional abnormalities in ASD from a single transient perspective, emphasizing the importance of the DMN in abnormal dynamic functional activity in ASD and suggesting that temporally dynamic techniques offer novel insights into time-varying neural processes.

PMID:38814465 | DOI:10.1007/s00787-024-02474-y

Cereb Cortex. 2024 May 2;34(5):bhae214. doi: 10.1093/cercor/bhae214.

ABSTRACT

A multitude of factors are associated with the symptoms of post-traumatic stress disorder. However, establishing which predictors are most strongly associated with post-traumatic stress disorder symptoms is complicated because few studies are able to consider multiple factors simultaneously across the biopsychosocial domains that are implicated by existing theoretical models. Further, post-traumatic stress disorder is heterogeneous, and studies using case-control designs may obscure which factors relate uniquely to symptom dimensions. Here we used Bayesian variable selection to identify the most important predictors for overall post-traumatic stress disorder symptoms and individual symptom dimensions in a community sample of 569 adults (18 to 85 yr of age). Candidate predictors were selected from previously established risk factors relevant for post-traumatic stress disorder and included psychological measures, behavioral measures, and resting state functional connectivity among brain regions. In a follow-up analysis, we compared results controlling for current depression symptoms in order to examine specificity. Poor sleep quality and dimensions of temperament and impulsivity were consistently associated with greater post-traumatic stress disorder symptom severity. In addition to self-report measures, brain functional connectivity among regions commonly ascribed to the default mode network, central executive network, and salience network explained the unique variability of post-traumatic stress disorder symptoms. This study demonstrates the unique contributions of psychological measures and neural substrates to post-traumatic stress disorder symptoms.

PMID:38813966 | DOI:10.1093/cercor/bhae214