Brain Functional Connectivity Research Articles

Connector hubs in semantic network contribute to creative thinking.

Semantic memory offers a rich repository of raw materials (e.g., various concepts and connections between concepts) for creative thinking, represented as a semantic network. Similar to other networks, the semantic network exhibits a modular structure characterized by modules with dense internal connections and sparse connections between them. This organizational principle facilitates the routine storage and retrieval of information but may impede creativity. The present study investigated the effect of hub concepts with varying connection patterns on creative thinking from the perspective of a modular structured semantic network. By analyzing a large-scale semantic network, connector hubs (C-hubs) and provincial hubs (P-hubs) were identified based on their intra- and intermodule connections. These hubs were used as cue words in the alternative uses task, a widely used measure of creative thinking. Across four experiments, behavioral and neural evidence indicated that C-hubs facilitate the generation of more novel and remote ideas compared to P-hubs. However, this effect is predominantly observed in the early stage of the creative thinking process, involving changes in brain activation and functional connectivity in core regions of the default mode network and the frontoparietal network, including the dorsolateral prefrontal cortex, angular gyrus, and precuneus. Neural findings suggest that the superior performance of C-hubs relies on stronger interactions between automatic spreading activation, controlled semantic retrieval, and attentional regulation of salient information. These results provide insight into how concepts with varying semantic connection patterns facilitate and constrain different stages of the creative thinking process through the modular structure of semantic network. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A spatially constrained independent component analysis jointly informed by structural and functional network connectivity

Abstract There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. The brain connectivity of different modalities provides an insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multimodal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model estimates ICNs at the subject level using a multiobjective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multimodal ICNs revealed enhanced functional coupling between ICNs with higher structural connectivity, improved modularity, and network distinction, particularly in schizophrenia. Statistical analysis of group differences showed more significant differences in the proposed model compared with the unimodal model. In summary, the sfCICA model showed benefits from being jointly informed by structural and functional connectivity. These findings suggest advantages in simultaneously learning effectively and enhancing connectivity estimates using structural connectivity.

Two-photon all-optical neurophysiology for the dissection of larval zebrafish brain functional and effective connectivity

One of the most audacious goals of modern neuroscience is unraveling the complex web of causal relations underlying the activity of neuronal populations on a whole-brain scale. This endeavor, which was prohibitive only a couple of decades ago, has recently become within reach owing to the advancements in optical methods and the advent of genetically encoded indicators/actuators. These techniques, applied to the translucent larval zebrafish have enabled recording and manipulation of the activity of extensive neuronal populations spanning the entire vertebrate brain. Here, we present a custom two-photon optical system that couples light-sheet imaging and 3D excitation with acousto-optic deflectors for simultaneous high-speed volumetric recording and optogenetic stimulation. By employing a zebrafish line with pan-neuronal expression of both the calcium reporter GCaMP6s and the red-shifted opsin ReaChR, we implemented a crosstalk-free, noninvasive all-optical approach and applied it to reconstruct the functional and effective connectivity of the left habenula.

Functional connectivity compensation in Parkinson's disease with freezing of gait.

Freezing of gait (FOG) is a disabling motor symptom prevalent in patients with Parkinson's disease (PD); however, its pathophysiological mechanisms are poorly understood. This study aimed to investigate whole-brain functional connectivity (FC) pattern alterations in PD patients with FOG. A total of 18 PD patients, 10 with FOG (PD-FOG) and 8 without FOG (PD-nFOG), and 10 healthy controls were enrolled. High-resolution 3D T1-weighted and resting-state functional MRI (rs-fMRI) data were obtained from all participants. The groups' internetwork connectivity differences were explored with rs-fMRI FC using seed-based analysis and graph theory. Multiple linear regression analysis estimated the relationship between FC changes and clinical measurements. Rs-fMRI analysis demonstrated alterations in FC in various brain regions between the three groups. Freezing of Gait Questionnaire severity was correlated with decreased brain functional connection between Vermis12 and the left temporal occipital fusiform cortex (r = -0.82, P < .001). Graph theory topological metrics indicated a decreased clustering coefficient in the right superior temporal gyrus in the PD-nFOG group. PD-FOG patients exhibited a compensatory increase in connectivity between the left inferior frontal gyrus language network and the postcentral gyrus compared to PD-nFOG patients. Further, the decreased connection between Vermis 12 and the left temporal occipital fusiform cortex may serve as a potential neuroimaging biomarker for tracking PD-FOG and distinguishing between PD subtypes.

Multi-view brain functional connectivity and hierarchical fusion for EEG-based emotion recognition

Emotional states evolve gradually from their onset to full manifestation, reflected in changes in brain functional connectivity. Existing research often focuses only on the final emotional state, missing the rich, dynamic information of emotional evolution. This paper proposes a novel network framework leveraging global and local brain functional connectivity through a multi-view approach. We introduce a global and local connectivity modeling method using electroencephalogram signals to simulate brain connectivity changes across time and specific moments. A residual-based local detail feature extractor combined with a coordinate attention module captures long-range dependencies and maintains positional accuracy. A global feature extractor is also designed for the effective extraction of emotional information. Finally, a hierarchical multi-scale cross-attention feature fusion module integrates features across different levels to enhance emotion recognition performance and robustness. Extensive experiments on three publicly available datasets demonstrate the framework’s superiority and generalization capability.

Functional brain connectivity predictors of prospective substance use initiation and their environmental correlates

BackgroundEarly substance use initiation (SUI) places youth at substantially higher risk for later substance use disorders. Furthermore, adolescence is a critical period for the maturation of brain networks, the pace and magnitude of which are susceptible to environmental influences and may shape risk for SUI. MethodsWe examined whether patterns of functional brain connectivity during rest (rsFC), measured longitudinally in pre-and-early adolescence, can predict future SUI. Next, in an independent sub-sample, we tested whether these patterns are associated with earlier environmental exposures, specifically neighborhood pollution and socioeconomic dimensions. We utilized data from the Adolescent Brain Cognitive Development (ABCD) Study®. SUI was defined as first-time use of at least one full dose of alcohol, nicotine, cannabis, or other drugs. We created a control group (N = 228) of participants without SUI who were matched with the SUI group (N = 233) on age, sex, race/ethnicity, and parental income and education. ResultsMultivariate analysis showed that whole-brain rsFC from 9-10 to 11-12 years of age (prior to SUI) prospectively differentiated the SUI and control groups. The SUI-related rsFC pattern was also related to aging in both groups, suggesting a pattern of accelerated maturation in the years prior to SUI. This same pattern of rsFC was predicted by higher pollution, but not neighborhood disadvantage (adjusted for family socioeconomic factors) in an independent sub-sample (N = 2,854). ConclusionBrain functional connectivity patterns in early adolescence that are linked to accelerated maturation can predict SUI in youth and are associated with exposure to pollution.

Polyconnectomic scoring of functional connectivity patterns across eight neuropsychiatric and three neurodegenerative disorders

BackgroundNeuropsychiatric and neurodegenerative disorders involve diverse changes in brain functional connectivity. As an alternative to approaches searching for specific mosaic patterns of affected connections and networks, we used polyconnectomic scoring to quantify disorder-related whole-brain connectivity signatures into interpretable, personalized scores. MethodsThe polyconnectomic score (PCS) measures the extent to which an individual's functional connectivity (FC) mirrors the whole-brain circuitry characteristics of a trait. We computed PCS for eight neuropsychiatric conditions (attention-deficit/hyperactivity disorder, anxiety-related disorders, autism spectrum disorder, obsessive-compulsive disorder, bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) and three neurodegenerative conditions (Alzheimer's disease, frontotemporal dementia, and Parkinson’s disease) across 22 datasets with resting-state functional MRI of 10,667 individuals (5,325 patients, 5,342 controls). We further examined PCS in 26,673 individuals from the population-based UK Biobank cohort. ResultsPCS was consistently higher in out-of-sample patients across six of the eight neuropsychiatric and across all three investigated neurodegenerative disorders ([min, max]: AUC = [0.55, 0.73], pFDR = [1.8 x 10-16, 4.5 x 10-2]). Individuals with elevated PCS levels for neuropsychiatric conditions exhibited higher neuroticism (pFDR < 9.7 x 10-5), lower cognitive performance (pFDR < 5.3 x 10-5), and lower general wellbeing (pFDR < 9.7 x 10-4). ConclusionsOur findings reveal generalizable whole-brain connectivity alterations in brain disorders. PCS effectively aggregates disorder-related signatures across the entire brain into an interpretable, subject-specific metric. A toolbox is provided for PCS computation.

Modulation effects of repetitive transcranial magnetic stimulation on target and indirect target nodes in patients with major depressive disorder

Clinical studies intensively highlight two critical brain regions, i,e, dorsal lateral prefrontal cortex (DLPFC) (target node) and subgenual anterior cingulate cortex (sgACC) (indirect target node) for the treatment of neuroimaging-guided repetitive transcranial magnetic stimulation (rTMS) in major depressive disorder (MDD). However, it remains unclear whether the clinical rTMS treatment could modulate the activity of the target and indirect target nodes in MDD patients. We aim to identify the rTMS-induced alteration of brain local and functional connectivity (FC) activities in the target and indirect target nodes. 38 patients with MDD were recruited for taking part in the 2-week rTMS treatment. We identified left DLPFC and right sgACC as the target and indirect target nodes for each participant, using the neuroimaging guided method, and further explored the rTMS-induced modulation on the brain functional activity of the two nodes. Ultimately, 28 patients were included in the analysis. We found that subjects had significant improvement in depressive symptoms, and their brain functional activities were reorganized. rTMS reduced the FC activity between the target and indirect target nodes, while the brain local activity in these nodes did not show rTMS-induced changes. The FC reduction was not associated with improvement in depressive symptoms. These results confirmed the clinical significance of the target node (DLPFC) and indirect target node (sgACC) in the rTMS treatment of MDD, and further shed light on the brain functional reorganization underpinning clinical practice of rTMS.

Alterations in brain activity and functional connectivity originating residual inhibition of tinnitus induced by tailor-made notched music training.

Tinnitus arises from the intricate interplay of multiple, parallel but overlapping networks, involving at least neuroplastic changes in the auditory and non-auditory activity. Tailor-made notched music training (TMNMT) is a promising therapy for tinnitus. Residual inhibition (RI) is one of the rare interventions capable of temporarily alleviating tinnitus, which is a useful technique that can be applied to tinnitus research and explore tinnitus mechanisms. To our knowledge, this study is the first to investigate the neural mechanisms underlying the RI effect of TMNMT through analysis of neural source activity and functional connectivity of EEG. Forty-four participants with tinnitus were divided into TMNMT group (twenty-two participants; ECnm, NMnm, RInm represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by TMNMT music, respectively) and Placebo control group (twenty-two participants; ECpb, PBpb, RIpb represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by Placebo music, respectively) in a single-blind manner. Source localization analysis revealed that RI effect of TMNMT significantly increased in current density at the delta band for the insula, subgenual anterior cingulate cortex (sgACC), parahippocampus (PHC), and secondary auditory cortex (AⅡ), and significantly increased in current density at the theta band for the sgACC, and significantly decreased in current density at the alpha band for the precuneus, PHC, primary and secondary auditory cortex (AⅠ and AⅡ). Meanwhile, RI effect of Placebo significantly decreased in current density at the alpha band for the PHC. Functional connectivity analysis demonstrated that RI effect of TMNMT significantly increased in phase coherence between the left AⅡ and the right sgACC; and between the left PHC and the left retrosplenial cortex (RSC) at the theta band. It significantly decreased in phase coherence between the left PHC and the right precuneus, the right posterior cingulate cortex (PCC), the right AⅡ; between the right PHC and the right PCC; and between the right PCC and the right AⅡ at the alpha band. RI effect of Placebo significantly increased in phase coherence between the left insula and the right precuneus, the left PHC, the right PHC, the left AⅠ, the left AⅡ; between the left sgACC and the right PHC; between the left AⅡ and the right PHC, the left PCC at the delta band. It was also found that the current density of sgACC was significant positively correlated with the tinnitus evaluation indicators (Loudness, VAS, THI, TFI) at the alpha band in TMNMT group. These results suggested that TMNMT, a novel music therapy for tinnitus, had a stronger ability of residual inhibition (RI), which RI effect of TMNMT not only participated in the activity of auditory network (AⅠ, AⅡ), but also extends to non-auditory network, particularly higher-level auditory association cortices such as the sgACC, PHC and PCC. The current study provided valuable experimental evidence and practical prospects for the potential applications of TMNMT in tinnitus treatment.

Characteristic Changes of Prefrontal and Motor Areas in Patients with Type 2 Diabetes and Major Depressive Disorder During a Motor Task of Tai Chi Chuan: A Functional Near-Infrared Spectroscopy Study.

This cross-sectional study aims to identify the characteristic changes of prefrontal and motor areas during a tai chi chuan task in patients with Type 2 diabetes mellitus (T2DM) and major depressive disorder (MDD) using wearable functional near-infrared spectroscopy (fNIRS). Three parallel groups (T2DM with DD group, T2DM group, and healthy group) were recruited from December 10, 2022, to May 31, 2023. Participants in three groups conducted a motor task of tai chi chuan designed by Eprime 3.0, and fNIRS was used to monitor the brain activation, functional connectivity (FC), and lateralization of prefrontal and motor areas. Correlation analyses were performed to examine the relationship between depressive symptoms and the function of prefrontal and motor areas. Ninety elder adults (aged ≥ 60), including 30 patients with T2DM and MDD, 30 patients with T2DM, and 30 healthy subjects, were enrolled. In contrast with the patients with T2DM and healthy subjects, the patients with T2DM and MDD had decreased activation and abnormal lateralization in prefrontal and motor areas and decreased FC among supplementary motor area, motor area, and dorsolateral prefrontal cortex (DLPFC). Furthermore, the oxyhemoglobin (HbO2) concentration value of DLPFC in patients with T2DM and MDD was negatively associated with scores of Hamilton Depression Scale-24 (HAMD-24). Patients with T2DM and MDD had characteristic functional changes in prefrontal and motor areas. DLPFC may be a potential target of diagnosis and intervention for patients with T2DM and MDD.

Analysis of brain network differences in the active, motor imagery, and passive stoke rehabilitation paradigms based on the task-state EEG

Different movement paradigms have varying effects on stroke rehabilitation, and their mechanisms of action on the brain are not fully understood. This study aims to investigate disparities in brain network and functional connectivity of three movement paradigms (active, motor imagery, passive) on stroke recovery. EEG signals were recorded from 11 S patients (SP) and 13 healthy controls (HC) during fist clenching and opening tasks under the three paradigms. Brain networks were constructed to analyze alterations in brain network connectivity, node strength (NS), clustering coefficients (CC), characteristic path length (CPL), and small-world index(S). Our findings revealed increased activity in the contralateral motor area in SP and higher activity in the ipsilateral motor area in HC. In the beta band, SP exhibited significantly higher CC in motor imagery (MI) than in active and passive tasks. Furthermore, the small world index of SP during MI tasks in the beta band was significantly smaller than in the active and passive tasks. NS in the gamma band for SP during the MI paradigm was significantly higher than in the active and passive paradigms. These findings suggest reorganization within both ipsilateral and contralateral motor areas of stroke patients during MI tasks, providing evidence for neural restructuring. Collectively, these findings contribute to a deeper understanding of task-state brain network changes and the rehabilitative mechanism of MI on motor function.

Exploring nonlinear dynamics in brain functionality through phase portraits and fuzzy recurrence plots.

Much of the complexity and diversity found in nature is driven by nonlinear phenomena, and this holds true for the brain. Nonlinear dynamics theory has been successfully utilized in explaining brain functions from a biophysics standpoint, and the field of statistical physics continues to make substantial progress in understanding brain connectivity and function. This study delves into complex brain functional connectivity using biophysical nonlinear dynamics approaches. We aim to uncover hidden information in high-dimensional and nonlinear neural signals, with the hope of providing a useful tool for analyzing information transitions in functionally complex networks. By utilizing phase portraits and fuzzy recurrence plots, we investigated the latent information in the functional connectivity of complex brain networks. Our numerical experiments, which include synthetic linear dynamics neural time series and a biophysically realistic neural mass model, showed that phase portraits and fuzzy recurrence plots are highly sensitive to changes in neural dynamics and can also be used to predict functional connectivity based on structural connectivity. Furthermore, the results showed that phase trajectories of neuronal activity encode low-dimensional dynamics, and the geometric properties of the limit-cycle attractor formed by the phase portraits can be used to explain the neurodynamics. Additionally, our results showed that the phase portrait and fuzzy recurrence plots can be used as functional connectivity descriptors, and both metrics were able to capture and explain nonlinear dynamics behavior during specific cognitive tasks. In conclusion, our findings suggest that phase portraits and fuzzy recurrence plots could be highly effective as functional connectivity descriptors, providing valuable insights into nonlinear dynamics in the brain.

Structural and functional correlates of olfactory reward processing in behavioral variant frontotemporal dementia

The behavioral variant of frontotemporal dementia (bvFTD) includes symptoms that reflect altered pursuit of rewards, including food, alcohol, and money. Little is known, however, about how these reward changes relate to atrophy and functional connectivity within reward-related regions. The goal of this study was to examine the structural and functional correlates of valence perception for olfactory rewards in 24 patients with bvFTD. Regression analysis of resting-state brain functional connectivity indicated that more positive valence ratings of olfactory stimuli were predicted by ventral pallidum connectivity to other reward circuit regions, particularly functional connectivity between ventral pallidum and bilateral anterior cingulate cortex/ventromedial prefrontal cortex. Structural analysis showed that atrophy of the anterior cingulate cortex was also significantly associated with perceiving stimuli as more rewarding. Finally, there was a significant interaction between ventral pallidum connectivity and atrophy of the anterior cingulate cortex. More specifically, the ventral pallidum connectivity had a greater effect on the positive perception of olfactory stimuli in the setting of low anterior cingulate cortex volume. These findings indicate that atrophy and functional connectivity within reward-relevant regions exert independent and interacting effects on the perception of pleasantness in bvFTD, potentially due to changes in hedonic “liking” signals.

Effects of repetitive transcranial magnetic stimulation at different targets on brain function in stroke patients: a randomized controlled trial.

Repetitive transcranial magnetic stimulation (rTMS) can improve post stroke motor function. However, there is little research on targets. The purpose of this study is to investigate the effects of rTMS therapy with different targets on post stroke motor function and neural plasticity. Fifty-four subjects were randomly divided into M1 (Primary motor area) group, SMA (supplementary motor area) group and Sham group, and were given 10 Hz on the affected M1 area, SMA area and sham stimulation rTMS. The primary outcomes included Fugl-Meyer Assessment Upper Extremity Scale (FMA-UE), Fugl-Meyer Assessment Lower Extremity Scale (FMA-LE) and Berg balance scale (BBS). Secondary outcomes: amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo) and functional connectivity (FC) were analyzed by functional magnetic resonance imaging (fMRI) to evaluate brain functional activation and functional connectivity changes. The 2-way repeated-measures ANOVA revealed a significant group × time interaction (F = 23.494, p < 0.001; F = 10.801, p < 0.001; F = 17.812, p < 0.001) in the FMA-UE, FMA-LE and BBS scores. Post hoc analysis indicated that 4 weeks of SMA rTMS resulted in an increase in FMA-UE, FMA-LE and BBS scores compared with Sham group (p = 0.006; p = 0.033; p = 0.012), SMA group was significantly increased in BBS compared with M1 group (p = 0.034). Moreover, there were significant effects of time in all 3 groups in the FMA-UE, FMA-LE and BBS scores (p < 0.001). In addition, the increase of ALFF in the supramarginal gyrus on the affected side was correlated with better FMA-UE recovery, the increase of ALFF in the middle temporal gyrus and the middle frontal gyrus on the affected side was positively correlated with the improvement of BBS, and the ALFF in the cerebellum on the healthy side was negatively correlated with the improvement of BBS. There was a positive correlation between FC (SMA - ipsilateral cerebellum) changes and BBS changes in SMA group. In conclusion, SMA-rTMS intervention has a better recovery effect on motor dysfunction after stroke than Sham-rTMS. SMA-rTMS led to similar improvement on motor function but significantly greater improvement on balance compared to M1-rTMS, and this may pave a new way for stroke rehabilitation. Registration number: ChiCTR2200060955, https://www.chictr.org.cn/.

Functional brain alterations in COVID-19 patients using resting-state fMRI: a systematic review.

This study systematically reviews the available evidence on resting-state functional magnetic resonance imaging (rs-fMRI) related to neurological symptoms and cognitive declines in COVID-19 patients. We followed PRISMA guidelines and looked up the PubMed, and Scopus databases for articles search on COVID-19 patients with neurological impairments, and functional connectivity alteration using rs-fMRI technique. Articles published between January 1, 2020, and May 31, 2024, are included in this study. The Quality Assessment Tool for Observational Prospective and Cross-Sectional Studies from the National Heart, Lung, and Blood Institute (NHLBI) was used to assess the quality of papers. A total of 15 articles met the inclusion criteria. The result reveals that the most prevalent neurological impairment associated with COVID-19 was cognitive decline, encompassing issues in attention, memory, processing speed, executive functions, language, and visuospatial ability. The brain connectivity results reveal that two brain areas were functionally altered; the prefrontal cortex and parahippocampus. The functional connectivity mainly increased in the frontal, temporal, and anterior piriform cortex, and reduced in the cerebellum, superior orbitofrontal cortex, and middle temporal gyrus, which also correlated with cognitive decline. The findings of neurological symptoms indicate one study reported a Disorder of Consciousness (DoC), and four studies reported COVID-19 patients with olfactory dysfunction. The present study concludes that COVID-19 can alter brain functional connectivity and offers significant insight into how COVID-19 affects the neuronal foundation of cognitive decline and other neurological impairments.

Linear Monotonic Inter-electrode Associations as Quantitative EEG for Alcoholism Diagnosis

Alcohol use disorders (AUDs) are associated with alterations in EEG patterns that reflect underlying neural dysfunctions, such as impaired cognitive processing, reduced neural connectivity, and disrupted brain rhythms. These EEG alterations can be indicative of several brain disorders, including cognitive deficits, mood disorders, and neurodegenerative diseases, which are often exacerbated by chronic alcohol abuse. Cognitive impairment in alcoholic subjects is caused due to altered functional connectivity between brain regions that can be quantified using statistical measures such as correlation. This paper proposes novel Quantitative EEG (QEEG) features comprising the band-wise absolute value of inter-electrode correlations as a measure of brain functional connectivity to classify alcoholic and non-alcoholic EEG. The EEG signal is first decomposed into five frequency sub-bands. In each sub-band, the absolute value of the linear Pearson product-moment correlation coefficient is computed between time-series EEG recorded by electrode pairs placed over different brain regions. To reduce the dimensionality of the feature vector, an ensemble feature selection approach is adopted, utilizing ANOVA and Chi-square, in conjunction with greedy forward feature selection wrapper algorithm. Four classifiers, namely SVM, KNN, ANN and RF are utilized for the classification. Among them, the SVM classifier achieves the best classification accuracies of 100% on validation with test data and 99.58% on cross-validation with train data, respectively.

Altered functional brain connectivity, efficiency, and information flow associated with brain fog after mild to moderate COVID-19 infection.

COVID-19 is associated with increased risk for cognitive decline but very little is known regarding the neural mechanisms of this risk. We enrolled 49 adults (55% female, mean age = 30.7 ± 8.7), 25 with and 24 without a history of COVID-19 infection. We administered standardized tests of cognitive function and acquired brain connectivity data using MRI. The COVID-19 group demonstrated significantly lower cognitive function (W = 475, p < 0.001, effect size r = 0.58) and lower functional connectivity in multiple brain regions (mean t = 3.47 ±0.36, p = 0.03, corrected, effect size d = 0.92 to 1.5). Hypo-connectivity of these regions was inversely correlated with subjective cognitive function and directly correlated with fatigue (p < 0.05, corrected). These regions demonstrated significantly reduced local efficiency (p < 0.026, corrected) and altered effective connectivity (p < 0.001, corrected). COVID-19 may have a widespread effect on the functional connectome characterized by lower functional connectivity and altered patterns of information processing efficiency and effective information flow. This may serve as an adaptation to the pathology of SARS-CoV-2 wherein the brain can continue functioning at near expected objective levels, but patients experience lowered efficiency as brain fog.

Functional brain connectivity changes associated with day-to-day fluctuations in affective states

Affective neuroscience has traditionally relied on cross-sectional studies to uncover the brain correlates of affects, emotions, and moods. Such findings obfuscate intraindividual variability that may reveal meaningful changing affect states. The few functional magnetic resonance imaging longitudinal studies that have linked changes in brain function to the ebbs and flows of affective states over time have mostly investigated a single individual. In this study, we explored how the functional connectivity of brain areas associated with affective processes can explain within-person fluctuations in self-reported positive and negative affects across several subjects. To do so, we leveraged the Day2day dataset that includes 40 to 50 resting-state functional magnetic resonance imaging scans along self-reported positive and negative affectivity from a sample of six healthy participants. Sparse multivariate mixed-effect linear models could explain 15% and 11% of the within-person variation in positive and negative affective states, respectively. Evaluation of these models’ generalizability to new data demonstrated the ability to predict approximately 5% and 2% of positive and negative affect variation. The functional connectivity of limbic areas, such as the amygdala, hippocampus, and insula, appeared most important to explain the temporal dynamics of affects over days, weeks, and months.

Diagnosis of Major Depressive Disorder Based on Individualized Brain Functional and Structural Connectivity.

Traditional neuroimaging studies have primarily emphasized analysis at the group level, often neglecting the specificity at the individual level. Recently, there has been a growing interest in individual differences in brain connectivity. Investigating individual-specific connectivity is important for understanding the mechanisms of major depressive disorder (MDD) and the variations among individuals. To integrate individualized functional connectivity and structural connectivity with machine learning techniques to distinguish people with MDD and healthy controls (HCs). Prospective. A total of 182 patients with MDD and 157 HCs and a verification cohort including 54 patients and 46 HCs. 3.0 T/T1-weighted imaging, resting-state functional MRI with echo-planar sequence, and diffusion tensor imaging with single-shot spin echo. Functional and structural brain networks from rs-fMRI and DTI data were constructed, respectively. Based on these networks, individualized functional connectivity (IFC) and individualized structural connectivity (ISC) were extracted using common orthogonal basis extraction (COBE). Subsequently, multimodal canonical correlation analysis combined with joint independent component analysis (mCCA + jICA) was conducted to fusion analysis to identify the joint and unique independent components (ICs) across multiple modes. These ICs were utilized to generate features, and a support vector machine (SVM) model was implemented for the classification of MDD. The differences in individualized connectivity between patients and controls were compared using two-sample t test, with a significance threshold set at P < 0.05. The established model was tested and evaluated using the receiver operating characteristic (ROC) curve. The classification performance of the constructed individualized connectivity feature model after multisequence fusion increased from 72.2% to 90.3%. Furthermore, the prediction model showed significant predictive power for assessing the severity of depression in patients with MDD (r = 0.544). The integration of IFC and ISC through multisequence fusion enhances our capacity to identify MDD, highlighting the advantages of the individualized approach and underscoring its significance in MDD research. 1 TECHNICAL EFFICACY: Stage 2.

Exploring Nonlinear Dynamics In Brain Functionality Through Phase Portraits And Fuzzy Recurrence Plots.

Much of the complexity and diversity found in nature is driven by nonlinear phenomena, and this holds true for the brain. Nonlinear dynamics theory has been successfully utilized in explaining brain functions from a biophysics standpoint, and the field of statistical physics continues to make substantial progress in understanding brain connectivity and function. This study delves into complex brain functional connectivity using biophysical nonlinear dynamics approaches. We aim to uncover hidden information in high-dimensional and nonlinear neural signals, with the hope of providing a useful tool for analyzing information transitions in functionally complex networks. By utilizing phase portraits and fuzzy recurrence plots, we investigated the latent information in the functional connectivity of complex brain networks. Our numerical experiments, which include synthetic linear dynamics neural time series and a biophysically realistic neural mass model, showed that phase portraits and fuzzy recurrence plots are highly sensitive to changes in neural dynamics and can also be used to predict functional connectivity based on structural connectivity. Furthermore, the results showed that phase trajectories of neuronal activity encode low-dimensional dynamics, and the geometric properties of the limit-cycle attractor formed by the phase portraits can be used to explain the neurodynamics. Additionally, our results showed that the phase portrait and fuzzy recurrence plots can be used as functional connectivity descriptors, and both metrics were able to capture and explain nonlinear dynamics behavior during specific cognitive tasks. In conclusion, our findings suggest that phase portraits and fuzzy recurrence plots could be highly effective as functional connectivity descriptors, providing valuable insights into nonlinear dynamics in the brain.