Most recent paper

Hum Brain Mapp. 2024 Dec 1;45(17):e70075. doi: 10.1002/hbm.70075.

ABSTRACT

Post-traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Despite the high prevalence of PTE, current methods for predicting its occurrence remain limited. In this study, we aimed to identify imaging-based markers for the prediction of PTE using machine learning. Specifically, we examined three imaging features: Lesion volumes, resting-state fMRI-based measures of functional connectivity, and amplitude of low-frequency fluctuation (ALFF). We employed three machine-learning methods, namely, kernel support vector machine (KSVM), random forest, and an artificial neural network (NN), to develop predictive models. Our results showed that the KSVM classifier, with all three feature types as input, achieved the best prediction accuracy of 0.78 AUC (area under the receiver operating characteristic (ROC) curve) using nested cross-validation. Furthermore, we performed voxel-wise and lobe-wise group difference analyses to investigate the specific brain regions and features that the model found to be most helpful in distinguishing PTE from non-PTE populations. Our statistical analysis uncovered significant differences in bilateral temporal lobes and cerebellum between PTE and non-PTE groups. Overall, our findings demonstrate the complementary prognostic value of MR-based markers in PTE prediction and provide new insights into the underlying structural and functional alterations associated with PTE.

PMID:39560185 | PMC:PMC11574740 | DOI:10.1002/hbm.70075

Hum Brain Mapp. 2024 Dec 1;45(17):e70077. doi: 10.1002/hbm.70077.

ABSTRACT

One of the fundamental questions in real-time functional magnetic resonance imaging neurofeedback (rt-fMRI NF) investigations is the definition of a suitable neural target for training. Previously, we applied a meta-analytical approach to define a network-level target for connectivity-based rt-fMRI NF in substance use disorders. The analysis yielded consistent connectivity alterations between the insula and anterior cingulate cortex (ACC) as well as the dorsal striatum and the ACC. In the current investigation, we addressed the feasibility of regulating this network and its functional relevance using connectivity-based neurofeedback. In a double-blind, sham-controlled design, 60 nicotine users were randomly assigned to the experimental or sham control group for one NF training session. The preregistered primary outcome was defined as improved inhibitory control performance after regulation of the target network compared to sham control. Secondary outcomes were (1) neurofeedback-specific changes in functional connectivity of the target network; (2) changes in smoking behavior and impulsivity measures; and (3) changes in resting-state connectivity profiles. Our results indicated no differences in behavioral measures after receiving feedback from the target network compared to the sham feedback. Target network connectivity was increased during regulation blocks compared to rest blocks, however, the experimental and sham groups could regulate to a similar degree. Accordingly, the observed activation patterns may be related to the mental strategies used during regulation attempts irrespective of the group assignment. We discuss several crucial factors regarding the efficacy of a single-session connectivity-based neurofeedback for the target network. This includes high fluctuation in the connectivity values of the target network that may impact controllability of the signal. To our knowledge, this investigation is the first randomized, double-blind controlled real-time fMRI study in nicotine users. This raises the question of whether previously observed effects in nicotine users are specific to the neurofeedback signal or reflect more general self-regulation attempts.

PMID:39559854 | PMC:PMC11574450 | DOI:10.1002/hbm.70077

Heliyon. 2024 Oct 23;10(21):e39735. doi: 10.1016/j.heliyon.2024.e39735. eCollection 2024 Nov 15.

ABSTRACT

The human brain comprises large-scale networks that flexibly interact to support diverse cognitive functions and adapt to variability in daily life. The inferior parietal lobe (IPL) is a hub of multiple brain networks that sustain various cognitive domains. It remains unclear how networks respond to acute regional perturbations to maintain normal function. To provoke network-level adaptive responses to local inhibition, we combined offline transcranial magnetic stimulation (TMS) over left or right IPL with neuroimaging during attention, semantic and social cognition tasks, and rest. Across tasks, TMS specifically affected task-active network activity with inhibition and facilitation. Network interaction responses differed between rest and tasks. After TMS over both IPL regions, large-scale network interactions were exclusively facilitated at rest, but mainly inhibited during tasks. Overall, responses to TMS primarily occurred in and between domain-general default mode and frontoparietal subnetworks. These findings elucidate short-term adaptive plasticity in response to network node inhibition.

PMID:39559231 | PMC:PMC11570486 | DOI:10.1016/j.heliyon.2024.e39735

Sci Rep. 2024 Nov 18;14(1):28421. doi: 10.1038/s41598-024-79527-4.

ABSTRACT

Pupil size and blink rates are heritable but the extent to which they interact with one another has not been properly investigated. Though changes in pupil size due to eye blinks have been reported, they are considered a pupillary artifact. In this study we used the HCP 7T fMRI dataset with resting state eye-tracking data obtained in monozygotic and dizygotic twins to assess their heritability and their interactions. For this purpose, we characterized the pupil dilation (positive peak) and constriction (negative peak) that followed blink events, which we describe as blink-induced pupillary response (BIPR). We show that the BIPR is highly consistent with a positive dilatory peak (D-peak) around 500ms and a negative constricting peak (C-peak) around 1s. These patterns were reproducible within- and between-subjects across two time points and differed by vigilance state (vigilant versus drowsy). By comparing BIPR between monozygotic and dizygotic twins we show that BIPR have a heritable component with significant additive genetic (A) and environmental (E) factors dominating the structural equation models, particularly in the time-domain for both D- and C-peaks (a2 between 42 and 49%) and shared effects (C) as observed in the amplitude domain for the C-peak. Blink duration, pupil size and blink rate were also found to be highly heritable (a2 up to 62% for pupil size). Our study provides evidence of that shared environmental and additive genetic factors influence BIPR and indicates that BIPR should not be treated as a coincidental artefact. Instead BIPR appears to be a component of a larger oculomotor system that we label here as Oculomotor Adaptive System, that is genetically determined.

PMID:39557891 | PMC:PMC11574171 | DOI:10.1038/s41598-024-79527-4

Cogn Neurodyn. 2024 Oct;18(5):2419-2432. doi: 10.1007/s11571-024-10095-z. Epub 2024 Mar 23.

ABSTRACT

Electroencephalography-based (EEG) microstate analysis is a promising and widely studied method in which spontaneous cerebral activity is segmented into sub second level quasi-stable states and analyzed. Currently it is being widely explored due to increasing evidence of the association of microstates with cognitive functioning and large-scale brain networks identified by functional magnetic resonance imaging (fMRI). In our study using the four archetypal microstates (A, B, C and D), we investigated the changes in resting state EEG microstate dynamics in persons with temporal lobe epilepsy (TLE) and idiopathic generalized epilepsy (IGE) compared to healthy controls (HC). Machine learning was applied to study its feasibility in differentiating between different groups using microstate statistics. We found significant differences in all parameters related to Microstate D (fronto-parietal network) in TLE patients and Microstate B (visual processing) in IGE patients compared to HCs. Occurrence, duration and time coverage of Microstate B was highest in IGE when compared to the other groups. We also found significant deviations in transition probabilities for both epilepsy groups, particularly into Microstate C (salience network) in IGE. Classification accuracy into clinical groups was found to exceed 70% using microstate parameters which improved on incorporating neuropsychological test differences. To the best of our knowledge, the current study is the first to compare and validate the use of microstate features to discriminate between two disparate epilepsy syndromes (TLE, IGE) and HCs using machine learning suggesting that resting state EEG microstates can be used for endophenotyping and to study resting state dysfunction in epilepsy.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-024-10095-z.

PMID:39555277 | PMC:PMC11564422 | DOI:10.1007/s11571-024-10095-z

Cogn Neurodyn. 2024 Aug;18(4):1651-1669. doi: 10.1007/s11571-023-10039-z. Epub 2023 Dec 1.

ABSTRACT

Dynamic functional connectivity (DFC) analysis using functional magnetic resonance imaging (fMRI) technology has attracted increasing attention in revealing brain dynamics in recent years. Although the nonnegative matrix factorization (NMF) method was applied to dynamic subgraph analysis to reveal brain dynamics, its application in DFC analysis was largely limited due to its nonnegative constraint on the input data. This study proposed the extended NMF (eNMF) method that allowed the input matrix and decomposed basis matrix to have negative values without altering the NMF algorithm. The eNMF method was applied to DFC analysis of both simulated and real resting fMRI data. The simulated data demonstrated that eNMF successfully decomposed the mixed-sign matrix into one positive matrix and one mixed-sign matrix. In contrast to K-means, eNMF extracted more accurate brain state patterns in all cases and estimated better DFC temporal properties for uneven brain state distribution. The real resting-fMRI data demonstrated that eNMF can provide more temporal measures of DFC and was more sensitive to detect intergroup differences of DFC than K-means. Results of eNMF revealed that the female group possibly showed worse relaxation and produced stronger spontaneous cognitive processes although they tended to spend more time in relaxation state and less time in states relevant to cognitive processes in contrast to the male group.

PMID:39554797 | PMC:PMC11564474 | DOI:10.1007/s11571-023-10039-z

Ther Adv Psychopharmacol. 2024 Nov 16;14:20451253241298871. doi: 10.1177/20451253241298871. eCollection 2024.

ABSTRACT

BACKGROUND: Emotional dysregulation, particularly unconscious catastrophic cognitions, plays a pivotal role in the genesis of panic disorder (PD). However, no studies have yet applied the percentage of amplitude fluctuation (PerAF) metric in resting-state functional magnetic resonance imaging to examine spontaneous neural functioning and its relation to catastrophic cognitions in PD.

OBJECTIVES: To explore the interplay between resting-state neural activity, functional connectivity (FC), and unconscious emotion regulation in individuals with PD.

DESIGN: Cross-sectional study.

METHODS: The study encompassed 51 participants, including 26 PD patients and 25 healthy individuals. The PerAF algorithm was employed to explore the local spontaneous neural activity in PD. Regions exhibiting aberrant spontaneous neural activity were used as seed points for whole-brain FC analysis. Correlations were utilized to examine associations between local neural activity patterns and neurocognitive assessments in PD.

RESULTS: The study revealed that compared to healthy individuals, PD patients exhibited elevated PerAF values in key emotion-regulation-related brain regions, including the ventromedial prefrontal cortex (vmPFC), striatum, amygdala, dorsomedial prefrontal cortex (dmPFC), and cerebellum. In addition, the resting-state FC between vmPFC and precuneus, as well as between the cerebellum and precuneus, was weakened in PD patients. Furthermore, positive associations were noted between PerAF measurements of vmPFC and amygdala and catastrophizing scores.

CONCLUSION: PD involves regional and network-level alterations in resting-state brain activity. The fronto-striatal-limbic circuits play a critical role in catastrophic-style emotion regulation in PD patients. Reduced FC within the default mode network and cerebellum-default mode network may signify a coordination anomaly in introspection and cognitive activities in PD. These findings complement the model of implicit emotion regulation in PD and suggest potential intervention targets.

PMID:39552918 | PMC:PMC11569504 | DOI:10.1177/20451253241298871

Psychol Med. 2024 Nov 18:1-12. doi: 10.1017/S0033291724001685. Online ahead of print.

ABSTRACT

BACKGROUND: Major psychiatric disorders (MPDs) are delineated by distinct clinical features. However, overlapping symptoms and transdiagnostic effectiveness of medications have challenged the traditional diagnostic categorisation. We investigate if there are shared and illness-specific disruptions in the regional functional efficiency (RFE) of the brain across these disorders.

METHODS: We included 364 participants (118 schizophrenia [SCZ], 80 bipolar disorder [BD], 91 major depressive disorder [MDD], and 75 healthy controls [HCs]). Resting-state fMRI was used to caclulate the RFE based on the static amplitude of low-frequency fluctuation, regional homogeneity, and degree centrality and corresponding dynamic measures indicating variability over time. We used principal component analysis to obtain static and dynamic RFE values. We conducted functional and genetic annotation and enrichment analysis based on abnormal RFE profiles.

RESULTS: SCZ showed higher static RFE in the cortico-striatal regions and excessive variability in the cortico-limbic regions. SCZ and MDD shared lower static RFE with higher dynamic RFE in sensorimotor regions than BD and HCs. We observed association between static RFE abnormalities with reward and sensorimotor functions and dynamic RFE abnormalities with sensorimotor functions. Differential spatial expression of genes related to glutamatergic synapse and calcium/cAMP signaling was more likely in the regions with aberrant RFE.

CONCLUSIONS: SCZ shares more regions with disrupted functional integrity, especially in sensorimotor regions, with MDD rather than BD. The neural patterns of these transdiagnostic changes appear to be potentially driven by gene expression variations relating to glutamatergic synapses and calcium/cAMP signaling. The aberrant sensorimotor, cortico-striatal, and cortico-limbic integrity may collectively underlie neurobiological mechanisms of MPDs.

PMID:39552391 | DOI:10.1017/S0033291724001685

Alcohol Clin Exp Res (Hoboken). 2024 Nov 17. doi: 10.1111/acer.15488. Online ahead of print.

ABSTRACT

A family history of alcohol use disorder (AUD) is associated with a significantly increased risk of developing AUD in one's lifetime. The previously reviewed literature suggests there are structural and functional neurobiological markers associated with familial AUD, but to our knowledge, no recent review has synthesized the latest findings across neuroimaging studies in this at-risk population. For this narrative review, we conducted keyword searches in electronic databases to find cross-sectional and longitudinal studies (2015-present) that used magnetic resonance imaging (MRI), diffusion tensor imaging, task-based functional MRI (fMRI), and/or resting state functional connectivity MRI. These studies were used to identify gray matter, white matter, and brain activity markers of risk and resilience in family history positive (FHP) individuals with a family history of AUD. FHP individuals have greater early adolescent thinning of executive functioning (frontal lobe) regions; however, some studies have reported null effects or greater gray matter volume and thickness relative to family history negative (FHN) peers without familial AUD. FHP individuals also have white matter microstructure alterations, such as reduced integrity of fronto-striatal pathways. Recent fMRI studies have found greater inhibitory control activity in FHP individuals, while reward-related findings are mixed. A growing interest in identifying intrinsic connectivity differences between FHP and FHN individuals has emerged in recent years. Familial AUD is related to both structural and functional brain alterations. Research should continue to focus on (1) longitudinal analyses with larger samples, (2) assessment of personal substance use and prenatal exposure to alcohol, (3) the effects of comorbid familial psychopathology, (4) examination of sex-specific markers of risk and resilience, (5) neural predictors of alcohol use initiation, and (6) brain-behavior relationships. These efforts would aid the design of neurobiologically informed prevention and intervention efforts focused on this at-risk population.

PMID:39552054 | DOI:10.1111/acer.15488

J Affect Disord. 2024 Nov 15:S0165-0327(24)01869-X. doi: 10.1016/j.jad.2024.11.036. Online ahead of print.

ABSTRACT

BACKGROUND: This study assessed adolescent brain-behavior relationships between large-scale dynamic functional network connectivity (FNC) and an integrated attention-deficit/hyperactivity disorder (ADHD) phenotype, including measures of inattention, impulsivity/hyperactivity and emotional dysregulation. Despite emotion dysregulation being a core clinical feature of ADHD, studies rarely assess its impact on large-scale FNC.

METHODS: We conducted resting-state functional magnetic resonance imaging in 78 adolescents (34 with ADHD) and obtained experimental and self-reported measures of inattention, impulsivity/hyperactivity, and emotional reactivity. We used multivariate analyses to evaluate group differences in dynamic FNC between the default mode, salience and central executive networks, meta-state functional connectivity and ADHD symptomology.

RESULTS: We present two significant group*behavior effects. Compared to controls, adolescents with ADHD had 1) diminished salience network-centered dynamic FNC that was driven by an integrated ADHD phenotype (p < .004, r = 0.57) and 2) more variable patterns of global connectivity, as measured through meta-state analysis, which were driven by heightened emotional reactivity (p < .002, r = 0.63).

CONCLUSIONS: Atypical patterns of dynamic FNC in adolescents with ADHD are associated with the affective and cognitive components of ADHD symptomology. Limitations include sample size and self-reported measures of emotional reactivity.

PMID:39551190 | DOI:10.1016/j.jad.2024.11.036

Neuroscience. 2024 Nov 15;563:239-251. doi: 10.1016/j.neuroscience.2024.11.030. Online ahead of print.

ABSTRACT

Essential tremor with resting tremor (rET) and tremor-dominant Parkinson's disease (tPD) share many similar clinical symptoms, leading to frequent misdiagnoses. Functional connectivity (FC) matrix analysis derived from resting-state functional MRI (Rs-fMRI) offers a promising approach for early diagnosis and for exploring FC network pathogenesis in rET and tPD. However, methods relying solely on a single connection pattern may overlook the complementary roles of different connectivity patterns, resulting in reduced diagnostic differentiation. Therefore, we propose a multi-pattern connection Graph Convolutional Network (MCGCN) method to integrate information from various connection modes, distinguishing between rET and healthy controls (HC), tPD and HC, and rET and tPD. We constructed FC matrices using three different connectivity modes for each subject and used these as inputs to the MCGCN model for disease classification. The classification performance of the model was evaluated for each connectivity mode. Subsequently, gradient-weighted class activation mapping (Grad-CAM) was used to identify the most discriminative brain regions. The important brain regions identified were primarily distributed within cerebellar-motor and non-motor cortical networks. Compared with single-pattern GCN, our proposed MCGCN model demonstrated superior classification accuracy, underscoring the advantages of integrating multiple connectivity modes. Specifically, the model achieved an average accuracy of 88.0% for distinguishing rET from HC, 88.8% for rET from tPD, and 89.6% for tPD from HC. Our findings indicate that combining graph convolutional networks with multi-connection patterns can not only effectively discriminate between tPD, rET, and HC but also enhance our understanding of the functional network mechanisms underlying rET and tPD.

PMID:39550063 | DOI:10.1016/j.neuroscience.2024.11.030

Sleep Med. 2024 Nov 12;124:727-736. doi: 10.1016/j.sleep.2024.11.012. Online ahead of print.

ABSTRACT

BACKGROUND: The COVID-19 pandemic has led to long-term neurological and psychological effects, including sleep disturbances. While prior studies have identified altered brain function post-COVID-19, specific functional connectivity (FC) patterns predicting sleep quality after recovery remain unclear. This study aims to identify FC patterns associated with sleep quality two months after the first negative COVID-19 antigen test.

METHODS: Using a connectome-based predictive modeling (CPM) approach, we identified the functional connectome regulating sleep quality based on a 164-region parcellation. Significant connections were analyzed using mediation models to examine their role in the relationship between anxiety, depression, and sleep.

RESULTS: FC between the right cerebellar peduncle and the left VIII of the cerebellum, and between the left middle temporal pole (MTP) and left ventral tegmental area (VTA), significantly predicted Pittsburgh Sleep Quality Index (PSQI) scores for sleep disturbances two months post-recovery (q2 = 0.059, MSE = 0.154, p = 0.017, r = 0.350). Mediation analysis showed a significant indirect effect of FC between the left MTP and VTA on the relationship between generalized anxiety and sleep disturbances (indirect effect = 0.013, 95% CI = [0.002, 0.03], pfdr <0.05). FC between the right dorsal raphe nucleus and ipsilateral regions-including occipital, parietal, and temporal areas-predicted PSQI scores for daytime dysfunction (q2 = 0.092, MSE = 0.678, p = 0.025, r = 0.342).

CONCLUSION: Post-COVID-19 brain connectivity and anxiety predict sleep quality. These findings highlight the potential for targeted therapeutic strategies to improve sleep and identify patients at risk for prolonged disturbances through FC biomarkers.

PMID:39549632 | DOI:10.1016/j.sleep.2024.11.012

Geroscience. 2024 Nov 16. doi: 10.1007/s11357-024-01436-1. Online ahead of print.

ABSTRACT

Given the increasing number of older adults in society, there is a growing need for studies on changes in the aging brain. The aim of this research is to investigate the effective connectivity of different age groups using resting-state functional magnetic resonance imaging (fMRI) and graph theory. By examining connectivity in different age groups, a better understanding of age-related changes can be achieved. Lifespan pilot data from the Human Connectome Project (HCP) were used to examine dynamic effective connectivity (dEC) changes across different age groups. The Granger causality method with time windowing was employed to calculate dEC. After extracting graph measures, statistical analyses were performed to compare the age groups. Support vector machine and decision tree classifiers were used to classify the different age groups based on the extracted graph measures. Based on the obtained results, it can be concluded that there are significant differences in the effective connectivity among the three age groups. Statistical analyses revealed disassortativity. The global efficiency exhibited a decreasing trend, and the transitivity measure showed an increasing trend with the advancing age. The decision tree classifier showed an accuracy of 86.67 % with Kruskal-Wallis selected features. This study demonstrates that changes in effective connectivity across different age brackets can serve as a tool for better understanding brain function during the aging process.

PMID:39549197 | DOI:10.1007/s11357-024-01436-1

Biol Psychiatry Cogn Neurosci Neuroimaging. 2024 Nov 13:S2451-9022(24)00337-9. doi: 10.1016/j.bpsc.2024.10.020. Online ahead of print.

ABSTRACT

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS), combined with exposure and response prevention (ERP), is a promising treatment modality for treatment-refractory obsessive-compulsive disorder (OCD). Yet, not all patients respond sufficiently to this treatment. We investigated whether brain activation during a symptom provocation task could predict treatment response.

METHODS: Sixty-one adults with OCD (22 male/ 39 female) underwent symptom provocation with OCD- and fear-related visual stimuli during fMRI prior to an 8-week combined rTMS and ERP treatment regimen. Participants received one of the three following rTMS treatments as part of a randomized controlled trial: (1) 10Hz rTMS (110% resting motor threshold (RMT)) to the left dorsolateral prefrontal cortex (DLPFC); (2) 10Hz rTMS (110% RMT) to the left pre-supplementary motor area (preSMA); or (3) 10Hz control rTMS (60% RMT) to the vertex. Multiple regression and correlation were used to examine the predictive value of task-related brain activation for treatment response in the following ROIs: dorsomedial prefrontal cortex, amygdala, DLPFC, and preSMA.

RESULTS: The different treatment groups responded equally to treatment. Higher pre-treatment task-related activation of the right amygdala to OCD-related stimuli showed a positive association with treatment response in all groups. Exploratory whole-brain analyses showed positive associations between activation in multiple task-relevant regions and treatment response. Only dorsal anterior cingulate cortex activation to fear-related stimuli showed a negative association with treatment outcome.

CONCLUSIONS: Higher pre-treatment right amygdala activation during symptom provocation predicts better treatment response to combined rTMS and ERP in OCD.

PMID:39547413 | DOI:10.1016/j.bpsc.2024.10.020

Burns. 2024 Nov 2;51(1):107305. doi: 10.1016/j.burns.2024.107305. Online ahead of print.

ABSTRACT

BACKGROUND: Pruritus, a common symptom of burn wounds, arises from skin tissue damage and abnormal tissue healing. Chronic post-burn pruritus (CPBP) is defined as itching that persists for six weeks or more. The brain mechanisms underlying CPBP are not understood adequately. This study aims to explore abnormal brain function in CPBP patients and identify potential pathogenesis of pruritus.

MATERIALS AND METHODS: Twenty patients with CPBP and twenty healthy controls (HCs) participated in the study and underwent resting-state functional magnetic resonance imaging (fMRI) scans. Brain activity was evaluated using regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF) measures. Preprocessing of fMRI data involved steps such as slice timing correction, motion correction, and nuisance regression to account for physiological noise and head motion. Statistical analyses included two-sample t-tests to compare ReHo, ALFF, and fALFF values between CPBP patients and HCs, with age as a covariate, and Spearman correlation analysis to explore relationships between brain activity measures and clinical characteristics.

RESULTS: The study revealed significant differences in brain activity between CPBP patients and HCs. CPBP patients exhibited altered higher ReHo in regions including the bilateral middle frontal gyrus, medial superior frontal gyrus, precuneus, left insula, right caudate, and bilateral cerebellar tonsils, with decreased ReHo in the right precentral gyrus. ALFF analysis showed increased activity in the bilateral middle frontal gyrus, medial superior frontal gyrus, right precuneus, and right inferior frontal gyrus, and decreased ALFF in the left precentral gyrus and right postcentral gyrus. fALFF values were notably higher in the bilateral medial superior frontal gyrus and precuneus. Several brain regions with significant differences in ReHo, ALFF, and fALFF were extensively correlated with the burned area and pruritus scale scores.

CONCLUSION: Our data suggest that patients with CPBP show alterations in ReHo, ALFF, and fALFF values primarily in brain regions associated with the default mode network and sensorimotor areas. These results may provide valuable insights relevant to the neuropathology of CPBP.

PMID:39546823 | DOI:10.1016/j.burns.2024.107305

Front Hum Neurosci. 2024 Oct 31;18:1501310. doi: 10.3389/fnhum.2024.1501310. eCollection 2024.

ABSTRACT

Resting-state functional magnetic resonance imaging (RS-fMRI) has been extensively utilized for noninvasive investigation of human brain activity. While studies employing simultaneous recordings of fMRI and electrophysiology have established a connection between the low-frequency fluctuation (< 0.1 Hz) observed in RS-fMRI and the local field potential (LFP), it remains unclear whether the RS-fMRI signal exhibits frequency-dependent modulation, which is a well-documented phenomenon in LFP. The present study concurrently recorded resting-state functional magnetic resonance imaging (RS-fMRI) and local field potentials (LFP) in the striatum of 8 rats before and after a pharmacological manipulation. We observed a highly similar frequency-dependent pattern of amplitude changes in both RS-fMRI and LFP following the manipulation, specifically an increase in high-frequency band amplitudes accompanied by a decrease in low-frequency band amplitudes. These findings provide direct evidence that the enhanced high-frequency fluctuations and reduced low-frequency fluctuations observed in RS-fMRI may reflect heightened neuronal activity.

PMID:39545149 | PMC:PMC11560898 | DOI:10.3389/fnhum.2024.1501310

BMC Complement Med Ther. 2024 Nov 14;24(1):396. doi: 10.1186/s12906-024-04703-y.

ABSTRACT

BACKGROUND: The hypothalamus has been recognized as a core structure in the sleep-wake cycle. However, whether the neuroplasticity of the hypothalamus is involved in the acupuncture treatment of insomnia remains elusive.

METHODS: We recruited 42 patients with chronic insomnia disorder (CID) and 23 matched healthy controls (HCs), with CID patients randomly assigned to receive real acupuncture (RA) or sham acupuncture (SA) for four weeks. Insomnia severity was evaluated using the Pittsburgh Sleep Quality Index (PSQI) score, and the resting-state functional connectivity (rsFC) of the hypothalamus was assessed via functional magnetic resonance imaging (fMRI).

RESULTS: In the cross-sectional investigation, CID patients showed increased rsFC between the medial hypothalamus (MH) and left lateral orbital frontal cortex (LOFC), and bilateral medial orbital frontal cortex (MOFC) compared to HCs. In the longitudinal experiment, PSQI scores significantly decreased in the RA group (p = 0.03) but not in the SA group. Interestingly, the increased MH-LOFC connectivity was found to be reduced following RA treatment. In addition, the altered rsFC of MH-LOFC significantly correlated with clinical improvement in the RA group (r = -0.692, p = 0.006).

CONCLUSION: This randomized neuroimaging study provides preliminary evidence that acupuncture may improve insomnia symptoms by restoring circuits associated with hypothalamic subregions.

TRIAL REGISTRATION: This trial has been registered on the Chinese Clinical Trial Registry ( www.chictr.org.cn ) with the identifier (ChiCTR1800017092). Registered date: 11/07/2018.

PMID:39543627 | PMC:PMC11566913 | DOI:10.1186/s12906-024-04703-y

Neuroimage. 2024 Nov 12;303:120925. doi: 10.1016/j.neuroimage.2024.120925. Online ahead of print.

ABSTRACT

The ongoing brain activity serves as a baseline that supports both internal and external cognitive processes. However, its precise nature remains unclear. Considering that people display various patterns of brain activity even when engaging in the same task, it is reasonable to believe that individuals possess their unique brain baseline pattern. Using spatial independent component analysis on a large sample of fMRI data from the Human Connectome Project (HCP), we found an individual-specific component which can be consistently extracted from either resting-state or different task states and is reliable over months. Compared to functional connectome fingerprinting, it is much more stable across different fMRI modalities. Its stability is closely related to high explained variance and is minimally influenced by factors such as noise, scan duration, and scan interval. We propose that this component underlying the ongoing activity represents an individual-specific baseline pattern of brain activity.

PMID:39542069 | DOI:10.1016/j.neuroimage.2024.120925

Cogn Neurodyn. 2024 Jun;18(3):813-827. doi: 10.1007/s11571-023-09941-3. Epub 2023 Feb 18.

ABSTRACT

The brain structure-function relationship is crucial to how the human brain works under normal or diseased conditions. Exploring such a relationship is challenging when using the 3-dimensional magnetic resonance imaging (MRI) functional dataset which is temporal dynamic and the structural MRI which is static. Partial Least Squares Correlation (PLSC) is one of the classical methods for exploring the joint spatial and temporal relationship. The goal of PLSC is to identify covarying patterns via linear voxel-wise combinations in each of the structural and functional data sets to maximize the covariance. However, existing PLSC cannot adequately deal with the unmatched temporal dimensions between structural and functional data sets. We proposed a new alternative variant of the PLSC, termed within-subject, voxel-wise, and constant-block PLSC, to address this problem. To validate our method, we used two data sets with weak and strong relationships in simulated data. Additionally, the analysis of real brain data was carried out based on gray matter volume hubs derived from sMRI and whole-brain voxel-wise measures from resting-state fMRI for aging effect based on healthy subjects aged 16-85 years. Our results showed that our constant-block PLSC can detect weak structure-function relationships and has better robustness to noise. In fact, it adequately unearthed the true simulated number of significant and more accurate latent variables for the simulated data and more meaningful LVs for the real data, with covariance improvement from 16.19 to 41.48% (simulated) and 13.29-53.68% (real data), respectively. More interestingly in the real data analysis, our method identified simultaneously the well-known brain networks such as the default mode, sensorimotor, auditory, and dorsal attention networks both functionally and structurally, implying the hubs we derived from gray matter volumes are the basis of brain function, supporting diverse functions. Constant-block PLSC is a feasible tool for analyzing the brain structure-function relationship.

PMID:39539980 | PMC:PMC11555187 | DOI:10.1007/s11571-023-09941-3

Front Pharmacol. 2024 Oct 30;15:1454628. doi: 10.3389/fphar.2024.1454628. eCollection 2024.

ABSTRACT

The modulation of social cognition is suggested as a possible mechanism contributing to the potential clinical efficacy of psychedelics in disorders involving socio-emotional and reward processing deficits. Resting-state functional Magnetic Resonance Imaging (rs-fMRI) can be used to detect changes in brain connectivity during psychedelic-induced states. Thus, this pharmacoimaging study investigates the effects of N,N-Dimethyltryptamine (DMT) on functional connectivity in brain areas relevant to social cognition, using a within-subject design in eleven healthy experienced users. The study included both an active and a control condition, conducted at different time points. The active condition involved DMT inhalation, while the control condition did not. Seed-based connectivity was measured for the two core regions involved in theory of mind and emotional processing, respectively, the posterior supramarginal gyrus and the amygdala. DMT increased supramarginal gyrus connectivity with the precuneus, posterior cingulate gyrus, amygdala, and orbitofrontal cortex. Additionally, increased connectivity emerged between the amygdala and orbitofrontal cortex. These results demonstrate that DMT modulates brain connectivity in socio-emotional and affective-value circuits, advancing our understanding of the neural mechanisms underlying the psychedelic experience and its potential therapeutic action.

PMID:39539622 | PMC:PMC11558042 | DOI:10.3389/fphar.2024.1454628