Resting-state Network Connectivity Research Articles

Effects of transcranial direct current stimulation on brain activity and cortical functional connectivity in children with autism spectrum disorders

IntroductionTranscranial direct current stimulation (tDCS) has emerged as a therapeutic option to mitigate symptoms in individuals with autism spectrum disorder (ASD). Our study investigated the effects of a two-week regimen of tDCS targeting the left dorsolateral prefrontal cortex (DLPFC) in children with ASD, examining changes in rhythmic brain activity and alterations in functional connectivity within key neural networks: the default mode network (DMN), sensorimotor network (SMN), and dorsal attention network (DAN).MethodsWe enrolled twenty-six children with ASD and assigned them randomly to either an active stimulation group (n=13) or a sham stimulation group (n=13). The active group received tDCS at an intensity of 1mA to the left DLPFC for a combined duration of 10 days. Differences in electrical brain activity were pinpointed using standardized low-resolution brain electromagnetic tomography (sLORETA), while functional connectivity was assessed via lagged phase synchronization.ResultsCompared to the typically developing children, children with ASD exhibited lower current source density across all frequency bands. Post-treatment, the active stimulation group demonstrated a significant increase in both current source density and resting state network connectivity. Such changes were not observed in the sham stimulation group.ConclusiontDCS targeting the DLPFC may bolster brain functional connectivity in patients with ASD, offering a substantive groundwork for potential clinical applications.

Multi-level prediction of substance use: Interaction of white matter integrity, resting-state connectivity and inhibitory control measured repeatedly in every-day life.

Substance use disorders are characterized by inhibition deficits related to disrupted connectivity in white matter pathways, leading via interaction to difficulties in resisting substance use. By combining neuroimaging with smartphone-based ecological momentary assessment (EMA), we questioned how biomarkers moderate inhibition deficits to predict use. Thus, we aimed to assess white matter integrity interaction with everyday inhibition deficits and related resting-state network connectivity to identify multi-dimensional predictors of substance use. Thirty-eight patients treated for alcohol, cannabis or tobacco use disorder completed 1 week of EMA to report substance use five times and complete Stroop inhibition testing twice daily. Before EMA tracking, participants underwent resting state functional MRIand diffusion tensor imaging (DTI) scanning. Regression analyses were conducted between mean Stroop performances and whole-brain fractional anisotropy (FA) in white matter. Moderation testing was conducted between mean FA within significant clusters as moderator and the link between momentary Stroop performance and use as outcome. Predictions between FA and resting-state connectivity strength in known inhibition-related networks were assessed using mixed modelling. Higher FA values in the anterior corpus callosum and bilateral anterior corona radiata predicted higher mean Stroop performance during the EMA week and stronger functional connectivity in occipital-frontal-cerebellar regions. Integrity in these regions moderated the link between inhibitory control and substance use, whereby stronger inhibition was predictive of the lowest probability of use for the highest FA values. In conclusion, compromised white matter structural integrity in anterior brain systems appears to underlie impairment in inhibitory control functional networks and compromised ability to refrain from substance use.

Childhood maltreatment and resting-state network connectivity: The risk-buffering role of positive parenting.

Unraveling the neurobiological foundations of childhood maltreatment is important due to the persistent associations with adverse mental health outcomes. However, the mechanisms through which abuse and neglect disturb resting-state network connectivity remain elusive. Moreover, it remains unclear if positive parenting can mitigate the negative impact of childhood maltreatment on network connectivity. We analyzed a cohort of 194 adolescents and young adults (aged 14-25, 47.42% female) from the Neuroscience in Psychiatry Network (NSPN) to investigate the impact of childhood abuse and neglect on resting-state network connectivity. Specifically, we examined the SAN, DMN, FPN, DAN, and VAN over time. We also explored the moderating role of positive parenting. The results showed that childhood abuse was linked to stronger connectivity within the SAN and VAN, as well as between the DMN-DAN, DMN-VAN, DMN-SAN, SAN-DAN, FPN-DAN, SAN-VAN, and VAN-DAN networks about 18 months later. Positive parenting during childhood buffered the negative impact of childhood abuse on network connectivity. To our knowledge, this is the first study to demonstrate the protective effect of positive parenting on network connectivity following childhood abuse. These findings not only highlight the importance of positive parenting but also lead to a better understanding of the neurobiology and resilience mechanisms of childhood maltreatment.

Multivariate association between psychosocial environment, behaviors, and brain functional networks in adolescent depression

BackgroundAdolescent depression shows high clinical heterogeneity. Brain functional networks serve as a powerful tool for investigating neural mechanisms underlying depression profiles. A key challenge is to characterize how variation in brain functional organization links to behavioral features and psychosocial environmental influences. MethodsWe recruited 80 adolescents with major depressive disorder (MDD) and 42 healthy controls (HCs). First, we estimated the differences in functional connectivity of resting-state networks (RSN) between the two groups. Then, we used sparse canonical correlation analysis to characterize patterns of associations between RSN connectivity and symptoms, cognition, and psychosocial environmental factors in MDD adolescents. Clustering analysis was applied to stratify patients into homogenous subtypes according to these brain-behavior-environment associations. ResultsMDD adolescents showed significantly hyperconnectivity between the ventral attention and cingulo-opercular networks compared with HCs. We identified one reliable pattern of covariation between RSN connectivity and clinical/environmental features in MDD adolescents. In this pattern, psychosocial factors, especially the interpersonal and family relationships, were major contributors to variation in connectivity of salience, cingulo-opercular, ventral attention, subcortical and somatosensory-motor networks. Based on this association, we categorized patients into two subgroups which showed different environment and symptoms characteristics, and distinct connectivity alterations. These differences were covered up when the patients were taken as a whole group. ConclusionThis study identified the environmental exposures associated with specific functional networks in MDD youths. Our findings emphasize the importance of the psychosocial context in assessing brain function alterations in adolescent depression and have the potential to promote targeted treatment and precise prevention.

Mindfulness-based cognitive therapy neurobiology in treatment-resistant obsessive-compulsive disorder: A domain-related resting-state networks approach

Mindfulness-based cognitive therapy (MBCT) stands out as a promising augmentation psychological therapy for patients with obsessive-compulsive disorder (OCD). To identify potential predictive and response biomarkers, this study examines the relationship between clinical domains and resting-state network connectivity in OCD patients undergoing a 3-month MBCT programme. Twelve OCD patients underwent two resting-state functional magnetic resonance imaging sessions at baseline and after the MBCT programme. We assessed four clinical domains: positive affect, negative affect, anxiety sensitivity, and rumination. Independent component analysis characterised resting-state networks (RSNs), and multiple regression analyses evaluated brain-clinical associations. At baseline, distinct network connectivity patterns were found for each clinical domain: parietal-subcortical, lateral prefrontal, medial prefrontal, and frontal-occipital. Predictive and response biomarkers revealed significant brain-clinical associations within two main RSNs: the ventral default mode network (vDMN) and the frontostriatal network (FSN). Key brain nodes —the precuneus and the frontopolar cortex— were identified within these networks. MBCT may modulate vDMN and FSN connectivity in OCD patients, possibly reducing symptoms across clinical domains. Each clinical domain had a unique baseline brain connectivity pattern, suggesting potential symptom-based biomarkers. Using these RSNs as predictors could enable personalised treatments and the identification of patients who would benefit most from MBCT.

Resting-state brain network connectivity is an independent predictor of responsiveness to language therapy in chronic post-stroke aphasia

Post-stroke aphasia recovery, especially in the chronic phase, is challenging to predict. Functional integrity of the brain and brain network topology have been suggested as biomarkers of language recovery. This study sought to investigate functional connectivity in four predefined brain networks (i.e., language, default mode, dorsal attention, and salience networks), in relation to aphasia severity and response to language therapy. Thirty patients with chronic post-stroke aphasia were recruited and received a treatment targeting word finding. Structural and functional brain scans were acquired at baseline and resting state functional connectivity for each network was calculated. Additionally, graph measures quantifying network properties were calculated for each network. These included global efficiency for all networks and average strength and clustering coefficient for the language network. Linear mixed effects models showed that mean functional connectivity in the default mode, dorsal attention, and salience networks as well as graph measures of all four networks are independent predictors of response to therapy. While greater mean functional connectivity and global efficiency of the dorsal attention and salience networks predicted greater treatment response, greater mean functional connectivity and global efficiency in the default mode network predicted poorer treatment response. Results for the language network were more nuanced with more efficient network configurations (as reflected in graph measures), but not mean functional connectivity, predicting greater treatment response. These findings highlight the prognostic value of resting-state functional connectivity in chronic treatment-induced aphasia recovery.

Alterations of resting-state functional network connectivity in patients with noise-induced hearing loss: A study based on independent component analysis.

Functional reorganization is a response to auditory deficits or deprivation, and less is known about the overall brain network alterations involving resting-state networks (RSNs) and multiple functional networks in patients with occupational noise-induced hearing loss (NIHL). So this study evaluated resting-state functional network connectivity (FNC) alterations in occupational NIHL using an independent component analysis (ICA). In total, 79 mild NIHL patients (MP), 32 relatively severe NIHL patients (RSP), and 84 age- and education- matched healthy controls (HC) were recruited. All subjects were tested using the Mini-mental State Examination scale, the tinnitus Handicap Inventory scale, the Hamilton Anxiety scale (HAMA) and scanned by T1-3DFSPGR, resting-state functional magnetic resonance imaging sequence in 3.0 T and analysed by the ICA. Seven RSNs were identified, compared with the HC, the MP showed increased FNC within the executive control network (ECN) and enhanced FNC within the default mode network (DMN) and the visual network (VN); compared with the HC, the RSP showed decreased FNC within the ECN and auditory network (AUN), DMN and VN; no significant changes in FNC were found in the MP compared with the RSP. Furthermore, the correlation analysis between the noise exposure time and hearing loss level, HAMA were both negative, and there were no significant correlations between the abnormal RSNs and the hearing level, noise exposure time and HAMA. These findings indicate that different degrees of NIHL involve different alterations in RSNs connectivity and may reveal the neural mechanisms related to emotion-related features and functional abnormalities following long-term NIHL.

The expert's knowledge combined with AI outperforms AI alone in seizure onset zone localization using resting state fMRI.

We evaluated whether integration of expert guidance on seizure onset zone (SOZ) identification from resting state functional MRI (rs-fMRI) connectomics combined with deep learning (DL) techniques enhances the SOZ delineation in patients with refractory epilepsy (RE), compared to utilizing DL alone. Rs-fMRI was collected from 52 children with RE who had subsequently undergone ic-EEG and then, if indicated, surgery for seizure control (n = 25). The resting state functional connectomics data were previously independently classified by two expert epileptologists, as indicative of measurement noise, typical resting state network connectivity, or SOZ. An expert knowledge integrated deep network was trained on functional connectomics data to identify SOZ. Expert knowledge integrated with DL showed a SOZ localization accuracy of 84.8 ± 4.5% and F1 score, harmonic mean of positive predictive value and sensitivity, of 91.7 ± 2.6%. Conversely, a DL only model yielded an accuracy of <50% (F1 score 63%). Activations that initiate in gray matter, extend through white matter, and end in vascular regions are seen as the most discriminative expert-identified SOZ characteristics. Integration of expert knowledge of functional connectomics can not only enhance the performance of DL in localizing SOZ in RE but also lead toward potentially useful explanations of prevalent co-activation patterns in SOZ. RE with surgical outcomes and preoperative rs-fMRI studies can yield expert knowledge most salient for SOZ identification.

Neural Mechanisms of Resting-State Networks and the Amygdala Underlying the Cognitive and Emotional Effects of Psilocybin

BackgroundSerotonergic psychedelics, such as psilocybin, alter perceptual and cognitive systems that are functionally integrated with the amygdala. These changes can alter cognition and emotions that are hypothesized to contribute to their therapeutic utility. However, the neural mechanisms of cognitive and subcortical systems altered by psychedelics are not well understood. MethodsWe used resting-state functional magnetic resonance images collected during a randomized, double-blind, placebo-controlled clinical trial of 24 healthy adults under 0.2 mg/kg psilocybin to estimate the directed (i.e., effective) changes between the amygdala and 3 large-scale resting-state networks involved in cognition. These networks are the default mode network, the salience network, and the central executive network. ResultsWe found a pattern of decreased top-down effective connectivity from these resting-state networks to the amygdala. Effective connectivity decreased within the default mode network and salience network but increased within the central executive network. These changes in effective connectivity were statistically associated with behavioral measures of altered cognition and emotion under the influence of psilocybin. ConclusionsOur findings suggest that temporary amygdala signal attenuation is associated with mechanistic changes to resting-state network connectivity. These changes are significant for altered cognition and perception and suggest targets for research investigating the efficacy of psychedelic therapy for internalizing psychiatric disorders. More broadly, our study suggests the value of quantifying the brain’s hierarchical organization using effective connectivity to identify important mechanisms for basic cognitive function and how they are integrated to give rise to subjective experiences.

Post-stroke upper limb recovery is correlated with dynamic resting-state network connectivity.

Motor recovery is still limited for people with stroke especially those with greater functional impairments. In order to improve outcome, we need to understand more about the mechanisms underpinning recovery. Task-unbiased, blood flow-independent post-stroke neural activity can be acquired from resting brain electrophysiological recordings and offers substantial promise to investigate physiological mechanisms, but behaviourally relevant features of resting-state sensorimotor network dynamics have not yet been identified. Thirty-seven people with subcortical ischaemic stroke and unilateral hand paresis of any degree were longitudinally evaluated at 3 weeks (early subacute) and 12 weeks (late subacute) after stroke. Resting-state magnetoencephalography and clinical scores of motor function were recorded and compared with matched controls. Magnetoencephalography data were decomposed using a data-driven hidden Markov model into 10 time-varying resting-state networks. People with stroke showed statistically significantly improved Action Research Arm Test and Fugl-Meyer upper extremity scores between 3 weeks and 12 weeks after stroke (both P < 0.001). Hidden Markov model analysis revealed a primarily alpha-band ipsilesional resting-state sensorimotor network which had a significantly increased life-time (the average time elapsed between entering and exiting the network) and fractional occupancy (the occupied percentage among all networks) at 3 weeks after stroke when compared with controls. The life-time of the ipsilesional resting-state sensorimotor network positively correlated with concurrent motor scores in people with stroke who had not fully recovered. Specifically, this relationship was observed only in ipsilesional rather in contralesional sensorimotor network, default mode network or visual network. The ipsilesional sensorimotor network metrics were not significantly different from controls at 12 weeks after stroke. The increased recruitment of alpha-band ipsilesional resting-state sensorimotor network at subacute stroke served as functionally correlated biomarkers exclusively in people with stroke with not fully recovered hand paresis, plausibly reflecting functional motor recovery processes.

Functional Brain Networks of Minor and Well-Structured Major Hallucinations in Parkinson's Disease.

Minor hallucinations (mHs) and well-structured major hallucinations (MHs) are common symptoms of Parkinson's disease (PD) psychosis. To investigate the resting-state networks (RSNs) in patients with PD without hallucinations (PD-nH), with mH (PD-mH), and with MH (PD-MH). A total of 73 patients with PD were enrolled (27 PD-nH, 23 PD-mH, and 23 PD-MH). Using seed-based functional connectivity analyses, we investigated the RSNs supposedly related to hallucinations in PD: the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN), ventral attention network (VAN), and visual network (VN). We compared the cognitive function and RSN connectivity among the three groups. In addition, we performed a seed-to-seed analysis to examine the inter-network connectivity within each group using the corresponding RSN seeds. PD-MH group had lower test scores for attention and visuospatial functions compared with those in the other groups. The connectivity of the right intracalcarine cortex within the DAN was lower in the PD-MH group than in the others. The PD-mH and PD-MH groups showed higher connectivity in the left orbitofrontal cortex within DMN compared with the PD-nH group, whereas the connectivity was lower in the right middle frontal gyrus (MFG) within ECN, precuneus cortex within VAN, right middle temporal gyrus and precuneus cortex within DAN, and left MFG within VN. The PD-mH and PD-MH groups showed different inter-network connectivity between the five RSNs, especially regarding DAN connectivity. DAN dysfunction may be a key factor in the progression from mH to MH in patients with PD. © 2023 International Parkinson and Movement Disorder Society.

Longitudinal resting-state network connectivity changes in electroconvulsive therapy patients compared to healthy controls

ObjectiveElectroconvulsive therapy (ECT) is effective for major depressive episodes. Understanding of underlying mechanisms has been increased by examining changes of brain connectivity but studies often do not correct for test-retest variability in healthy controls (HC). In this study, we investigated changes in resting-state networks after ECT in a multicenter study. MethodsFunctional resting-state magnetic resonance imaging data, acquired before start and within one week after ECT, from 90 depressed patients were analyzed, as well as longitudinal data of 24 HC. Group-information guided independent component analysis (GIG-ICA) was used to spatially restrict decomposition to twelve canonical resting-state networks. Selected networks of interest were the default mode network (DMN), salience network (SN), and left and right frontoparietal network (LFPN, and RFPN). Whole-brain voxel-wise analyses were used to assess group differences at baseline, group by time interactions, and correlations with treatment effectiveness. In addition, between-network connectivity and within-network strengths were computed. ResultsWithin-network strength of the DMN was lower at baseline in ECT patients which increased after ECT compared to HC, after which no differences were detected. At baseline, ECT patients showed lower whole-brain voxel-wise DMN connectivity in the precuneus. Increase of within-network strength of the LFPN was correlated with treatment effectiveness. We did not find whole-brain voxel-wise or between-network changes. ConclusionDMN within-network connectivity normalized after ECT. Within-network increase of the LFPN in ECT patients was correlated with higher treatment effectiveness. In contrast to earlier studies, we found no whole-brain voxel-wise changes, which highlights the necessity to account for test-retest effects.

Visual stimulation by extensive visual media consumption can be beneficial for motor learning

In this randomized controlled intervention trial, we investigated whether intense visual stimulation through television watching can enhance visual information processing and motor learning performance. 74 healthy young adults were trained in a motor skill with visual information processing demands while being accommodated in a controlled environment for five days. The experimental manipulation (n = 37) consisted of prolonged television watching (i.e., 8 h/day, + 62.5% on average) to induce intense exposure to visual stimulation. The control group (n = 37) did not consume visual media. The groups were compared by motor learning performance throughout the study as well as pre/post visual attention parameters and resting-state network connectivity in functional MRI. We found that the intervention group performed significantly better in the motor learning task (+ 8.21% (95%-CI[12.04, 4.31], t(70) = 4.23, p < 0.001) while showing an increased capacity of visual short-term memory (+ 0.254, t(58) = − 3.19, p = 0.002) and increased connectivity between visual and motor-learning associated resting-state networks. Our findings suggest that the human brain might enter a state of accentuated visuomotor integration to support the implementation of motor learning with visual information processing demands if challenged by ample input of visual stimulation. Further investigation is needed to evaluate the persistence of this effect regarding participants exposed to accustomed amounts of visual media consumption.Clinical Trials Registration: This trial was registered in the German Clinical Trials Register/Deutsches Register klinischer Studien (DRKS): DRKS00019955.

Neuroanatomical and Functional Dissociations between Variably Present Anterior Lateral Prefrontal Sulci.

The lateral prefrontal cortex (LPFC) is an evolutionarily expanded region in humans that is critical for numerous complex functions, many of which are largely hominoid specific. Although recent work shows that the presence or absence of specific sulci in anterior LPFC is associated with cognitive performance across age groups, it is unknown whether the presence of these structures relates to individual differences in the functional organization of LPFC. To fill this gap in knowledge, we leveraged multimodal neuroimaging data from two samples encompassing 82 young adult humans (aged 22-36 years) and show that the dorsal and ventral components of the paraintermediate frontal sulcus, or pimfs, present distinct morphological (surface area), architectural (thickness and myelination), and functional (resting-state connectivity networks) properties. We further contextualize the pimfs components within classic and modern cortical parcellations. Taken together, the dorsal and ventral pimfs components mark transitions in LPFC anatomy and function, across metrics and parcellations. These results emphasize that the pimfs is a critical structure to consider when examining individual differences in the anatomical and functional organization of LPFC and suggest that future individual-level parcellations could benefit from incorporating sulcal anatomy when delineating LPFC cortical regions.

2 Intentionality of Self-Generated Thought: Contributions of Mind Wandering to Creativity

Objective:Studies suggest that internally oriented cognitive processes are central to creativity. Here, we distinguish between intentional and unintentional forms of mind wandering and explore their component contributions to creativity. Furthermore, we describe resting-state connectivity profiles associated with these trait-level measures of mind wandering. Bearing in mind the role of the DMN in internally directed cognitive processes, we hypothesize that, in general, mind wandering will be associated with increased connectivity of voxels in the medial prefrontal and posterior cingulate cortices, key nodes in the DMN. We further hypothesize that intentional and unintentional mind wandering will be associated with distinct patterns of connectivity across DMN core regions.Participants and Methods:We used a sample of 155 healthy adults from the mind-brain-body dataset, all of whom completed resting-state fMRI scans and trait-level measures of mind wandering. We analyzed intentional and unintentional mind wandering tendencies using self-report measures. Next, we explored the relationship between mind wandering tendencies and creativity, as measured by a divergent thinking task. Additionally, we describe patterns of resting-state network connectivity associated with mind wandering, using graph theory (weighted degree) functional connectivity analysis.Results:At the behavioral level, results showed a significant positive association between creativity and both intentional and unintentional mind wandering. Neuroimaging analysis revealed higher weighted degree connectivity associated with both forms of mind wandering, implicating core regions of the default network and the left temporal pole. We observed topological connectivity differences within the default network: intentional mind wandering was associated with degree connectivity in posterior regions, whereas unintentional mind wandering showed greater involvement of prefrontal areas.Conclusions:In this study, we highlight patterns of resting-state network connectivity associated with intentional and unintentional mind wandering, and provide novel evidence of a link between mind wandering and creativity. These findings represent a promising step towards understanding the neurocognitive mechanisms that underlie productive mind wandering and demonstrate its relevance for the study of creative thinking.

Resting state network connectivity is associated with cognitive flexibility performance in youth in the Adolescent Brain Cognitive Development Study

Cognitive flexibility is an executive functioning skill that develops in childhood, and when impaired, has transdiagnostic implications for psychiatric disorders. To identify how intrinsic neural architecture at rest is linked to cognitive flexibility performance, we used the data-driven method of independent component analysis (ICA) to investigate resting state networks (RSNs) and their whole-brain connectivity associated with levels of cognitive flexibility performance in children. We hypothesized differences by cognitive flexibility performance in RSN connectivity strength in cortico-striatal circuitry, which would manifest via the executive control network, right and left frontoparietal networks (FPN), salience network, default mode network (DMN), and basal ganglia network. We selected participants from the Adolescent Brain Cognitive Development (ABCD) Study who scored at the 25th, (“CF-Low”), 50th (“CF-Average”), or 75th percentiles (“CF-High”) on a cognitive flexibility task, were early to middle puberty, and did not exhibit significant psychopathology (n = 967, 47.9% female; ages 9–10). We conducted whole-brain ICA, identifying 14 well-characterized RSNs. Groups differed in connectivity strength in the right FPN, anterior DMN, and posterior DMN. Planned comparisons indicated CF-High had stronger connectivity between right FPN and supplementary motor/anterior cingulate than CF-Low. CF-High had more anti-correlated connectivity between anterior DMN and precuneus than CF-Average. CF-Low had stronger connectivity between posterior DMN and supplementary motor/anterior cingulate than CF-Average. Post-hoc correlations with reaction time by trial type demonstrated significant associations with connectivity. In sum, our results suggest childhood cognitive flexibility performance is associated with DMN and FPN connectivity strength at rest, and that there may be optimal levels of connectivity associated with task performance that vary by network.

Abnormal functional connectivity in radiologically isolated syndrome: A resting-state fMRI study

Background: Radiologically isolated syndrome (RIS) patients might have psychiatric and cognitive deficits, which suggests an involvement of major resting-state functional networks. Notwithstanding, very little is known about the neural networks involved in RIS. Objective: To examine functional connectivity differences between RIS and healthy controls using resting-state functional magnetic resonance imaging (fMRI). Methods: Resting-state fMRI data in 25 RIS patients and 28 healthy controls were analyzed using an independent component analysis; in addition, seed-based correlation analysis was used to obtain more information about specific differences in the functional connectivity of resting-state networks. Participants also underwent neuropsychological testing. Results: RIS patients did not differ from the healthy controls regarding age, sex, and years of education. However, in memory (verbal and visuospatial) and executive functions, RIS patients’ cognitive performance was significantly worse than the healthy controls. In addition, fluid intelligence was also affected. Twelve out of 25 (48%) RIS patients failed at least one cognitive test, and six (24.0%) had cognitive impairment. Compared to healthy controls, RIS patients showed higher functional connectivity between the default mode network and the right middle and superior frontal gyri and between the central executive network and the right thalamus (pFDR < 0.05; corrected). In addition, the seed-based correlation analysis revealed that RIS patients presented higher functional connectivity between the posterior cingulate cortex, an important hub in neural networks, and the right precuneus. Conclusion: RIS patients had abnormal brain connectivity in major resting-state neural networks and worse performance in neurocognitive tests. This entity should be considered not an “incidental finding” but an exclusively non-motor (neurocognitive) variant of multiple sclerosis.

Locally Low-Rank Denoising of Multi-Echo Functional MRI Data With Application in Resting-State Analysis.

Locally low-rank (LLR) denoising of functional magnetic resonance imaging (fMRI) time series image data is extended to multi-echo (ME) data. The proposed method extends the capabilities of non-physiologic noise suppression beyond single-echo applications with a dedicated ME algorithm. Following an institutional review board (IRB) approved protocol, resting-state fMRI data were acquired in 7 healthy subjects. A compact 3T scanner enabled whole-brain acquisition of multiband ME fMRI data at high spatial resolution (1.4 × 1.4 × 2.8 mm 3 ) with a 1810 ms repetition time (TR). Image data were denoised with ME-LLR preceding functional processing. The results of connectivity maps generated from denoised data were compared with maps generated with equivalent processing of non-denoised images. To assess ME-LLR as a method to reduce scan time, comparisons were made between maps computed from image data with full and retrospectively truncated durations. Assessments were completed with seed-based connectivity analyses using echo-combined image data. In a feasibility assessment, nondenoised and denoised full-duration echo-combined data were equivalently processed with independent component analysis (ICA) and compared. ME-LLR denoising yielded strengthened resting-state network connectivity maps after nuisance regression and seed-based connectivity analysis. In assessing ME-LLR as a scan reduction mechanism, maps generated from denoised data at half scan time showed comparable quality with maps generated from full-duration, non-denoised data, at both single subject and group levels. ME-LLR substantially increased temporal signal-to-noise ratio (tSNR) for image data respective to each individual echo and for image data after nuisance regression. Among echo-specific image volumes, increases in tSNR yielded by ME-LLR were most pronounced for image data with the longest echo time and thereby lowest SNR. ICA showed resting-state networks consistently identified between non-denoised and denoised data, with clearer demarcation of networks for ME-LLR. ME-LLR is demonstrated to suppress non-physiologic noise, enhance functional connectivity map quality, and could potentially facilitate scan time reduction in ME-fMRI.

Functional connectivity mediates the relationship between cardiorespiratory fitness and stress in midlife.

BackgroundIncreasing evidence suggests that the relation between mental health and physical health is bidirectional and underpinned by complex neural systems. Cardiovascular fitness is a key measure of physical health but its relation to mental health is insufficiently examined. Characterizing the neural mechanisms by which cardiovascular fitness influences mental health could inform the development of strategies to promote mental health and minimize the risk of mental disorders. Methods and resultsThe relation between cardiorespiratory fitness, functional brain connectivity and mental health was studied in 418 healthy middle-aged (aged 40–65 years) adult participants of the Barcelona Brain Health Initiative (BBHI). Higher cardiorespiratory fitness, measured by VO₂ peak, was associated with lower symptoms of anxiety (β = −0.111, p = 0.017) and stress (β = −0.242, p = 0.002) scores, evaluated by the Depression Anxiety and Stress Scale (DASS-21) and its three subscales (stress, anxiety, and depression). Higher within-network functional connectivity of the Default Mode Network (DMN) was associated with higher VO₂ peak (β = 0.195, p = 0.002), and lower stress scores (β = −0.126, p = 0.011). In addition, higher functional connectivity between the Frontoparietal Network (FPN) and Salience Network (SN) was associated with higher VO₂ peak (β = 0.187, p = 0.002), and lower stress scores (β = −0.123, p = 0.016). Both within-DMN [ACME = −0.02 (-0.04,-0.00), p = 0.040] and between FPN-SN [ACME = −0.01 (-0.04,-0.00), p = 0.036] functional connectivity mediated the relationship between cardiorespiratory fitness and stress. ConclusionsThe relationship between the cardiorespiratory fitness and stress in middle-aged adults is mediated by functional connectivity of several intrinsic resting-state networks. These results highlight a potential mechanistic pathway through which higher cardiorespiratory fitness can positively impact brain health in midlife.

Acute effects of Δ9-tetrahydrocannabinol (THC) on resting state connectivity networks and impact of COMT genotype: A multi-site pharmacological fMRI study

BackgroundCannabis produces various acute psychotropic effects, with marked individual differences. Cannabis use is a risk factor for developing psychotic disorders. The main component responsible for these effects is Δ9-tetrahydrocannabinol (THC). Here we investigated the neural basis of acute THC effects and its modulation by catechol-methyl-transferase (COMT) Val158Met genotype. MethodsResting state functional MRI data of healthy occasional cannabis users were combined and re-analyzed from three double-blind, placebo-controlled, within-subject pharmacological functional magnetic resonance imaging studies (total N=87). Functional connectivity after placebo and THC was compared in three functional networks (salience, executive and default mode network) and a network implicated in psychosis (the hippocampus-midbrain-striatum network). COMT genotype modulation of subjective effects and connectivity was examined. ResultsTHC reduced connectivity in the salience network, specifically from the right insula to both the left insula and anterior cingulate cortex. We found a trend towards decreased connectivity in the hippocampus-midbrain-striatum network after THC. COMT genotype modulated subjective effects of THC, with strongest dysphoric reactions in Met/Met individuals. In addition, reduced connectivity after THC was demonstrated in the hippocampus-midbrain-striatum network of Met/Met individuals only. ConclusionsIn this large multisite study we found that THC robustly decreases connectivity in the salience network, involved in processing awareness and salient information. Connectivity changes in the hippocampus-midbrain-striatum network may reflect the acute psychotic-like effects of THC. COMT genotype modulation of THC’s impact on subjective effects and functional connectivity provides further evidence for involvement of prefrontal dopamine levels in the acute effects of cannabis.