Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses

Longitudinal hippocampal axis in large-scale cortical systems underlying development and episodic memory

The hippocampus is functionally specialized along its longitudinal axis with intricate interactions with cortical systems, which is crucial for understanding development and cognition. Using a well-established connectopic mapping technique on two large resting-state functional MRI datasets, we systematically quantified topographic organization of the hippocampal functional connectivity (hippocampal gradient) and its cortical interaction in developing brains. We revealed hippocampal functional hierarchy within the large-scale cortical brain systems, with the anterior hippocampus preferentially connected to an anterior temporal (AT) pathway and the posterior hippocampus embedded in a posterior medial (PM) pathway. We examined the developmental effects of the primary gradient and its whole-brain functional interaction. We observed increased functional specialization along the hippocampal long axis and found a general whole-brain connectivity shift from the posterior to the anterior hippocampus during development. Using phenotypic predictive modeling, we further delineated how the hippocampus is differentially integrated into the whole-brain cortical hierarchy underlying episodic memory and identified several key nodes within PM/AT systems. Our results highlight the importance of hippocampal gradient and its cortical interaction in development and for supporting episodic memory.

Large-scale brain connectivity changes following the administration of lysergic acid diethylamide, d-amphetamine, and 3,4-methylenedioxyamphetamine

Psychedelics have recently attracted significant attention for their potential to mitigate symptoms associated with various psychiatric disorders. However, the precise neurobiological mechanisms responsible for these effects remain incompletely understood. A valuable approach to gaining insights into the specific mechanisms of action involves comparing psychedelics with substances that have partially overlapping neurophysiological effects, i.e., modulating the same neurotransmitter systems. Imaging data were obtained from the clinical trial NCT03019822, which explored the acute effects of lysergic acid diethylamide (LSD), d-amphetamine, and 3,4-methylenedioxymethamphetamine (MDMA) in 28 healthy volunteers. The clinical trial employed a double-blind, placebo-controlled, crossover design. Herein, various resting-state connectivity measures were examined, including within-network connectivity (integrity), between-network connectivity (segregation), seed-based connectivity of resting-state networks, and global connectivity. Differences between placebo and the active conditions were assessed using repeated-measures ANOVA, followed by post-hoc pairwise t-tests. Changes in voxel-wise seed-based connectivity were correlated with serotonin 2 A receptor density maps. Compared to placebo, all substances reduced integrity in several networks, indicating both common and unique effects. While LSD uniquely reduced integrity in the default-mode network (DMN), the amphetamines, in contrast to our expectations, reduced integrity in more networks than LSD. However, LSD exhibited more pronounced segregation effects, characterized solely by decreases, in contrast to the amphetamines, which also induced increases. Across all substances, seed-based connectivity mostly increased between networks, with LSD demonstrating more pronounced effects than both amphetamines. Finally, while all substances decreased global connectivity in visual areas, compared to placebo, LSD specifically increased global connectivity in the basal ganglia and thalamus. These findings advance our understanding of the distinctive neurobiological effects of psychedelics, prompting further exploration of their therapeutic potential.

Seeing more than the Tip of the Iceberg: Approaches to Subthreshold Effects in Functional Magnetic Resonance Imaging of the Brain

Many functional magnetic resonance imaging (fMRI) studies and presurgical mapping applications rely on mass-univariate inference with subsequent multiple comparison correction. Statistical results are frequently visualized as thresholded statistical maps. This approach has inherent limitations including the risk of drawing overly-selective conclusions based only on selective results passing such thresholds. This article gives an overview of both established and newly emerging scientific approaches to supplement such conventional analyses by incorporating information about subthreshold effects with the aim to improve interpretation of findings or leverage a wider array of information. Topics covered include neuroimaging data visualization, p-value histogram analysis and the related Higher Criticism approach for detecting rare and weak effects. Further examples from multivariate analyses and dedicated Bayesian approaches are provided.

Alzheimer's disease genetic pathways impact cerebrospinal fluid biomarkers and imaging endophenotypes in non-demented individuals

INTRODUCTION

Unraveling how Alzheimer's disease (AD) genetic risk is related to neuropathological heterogeneity, and whether this occurs through specific biological pathways, is a key step toward precision medicine.

METHODS

We computed pathway-specific genetic risk scores (GRSs) in non-demented individuals and investigated how AD risk variants predict cerebrospinal fluid (CSF) and imaging biomarkers reflecting AD pathology, cardiovascular, white matter integrity, and brain connectivity.

RESULTS

CSF amyloidbeta and phosphorylated tau were related to most GRSs. Inflammatory pathways were associated with cerebrovascular disease, whereas quantitative measures of white matter lesion and microstructure integrity were predicted by clearance and migration pathways. Functional connectivity alterations were related to genetic variants involved in signal transduction and synaptic communication.

DISCUSSION

This study reveals distinct genetic risk profiles in association with specific pathophysiological aspects in predementia stages of AD, unraveling the biological substrates of the heterogeneity of AD-associated endophenotypes and promoting a step forward in disease understanding and development of personalized therapies.

HIGHLIGHTS

Polygenic risk for Alzheimer's disease encompasses six biological pathways that can be quantified with pathway-specific genetic risk scores, and differentially relate to cerebrospinal fluid and imaging biomarkers. Inflammatory pathways are mostly related to cerebrovascular burden. White matter health is associated with pathways of clearance and membrane integrity, whereas functional connectivity measures are related to signal transduction and synaptic communication pathways.

Dysconnectivity of the cerebellum and somatomotor network correlates with the severity of alogia in chronic schizophrenia

Recent fMRI resting-state findings show aberrant functional connectivity within somatomotor network (SMN) in schizophrenia. Moreover, functional connectivity aberrations of the motor system are often reported to be related to the severity of psychotic symptoms. Thus, it is important to validate those findings and confirm their relationship with psychopathology. Therefore, we decided to take an entirely data-driven approach in our fMRI resting-state study of 30 chronic schizophrenia outpatients and 30 matched control subjects. We used independent component analysis (ICA), dual regression, and seed-based connectivity analysis. We found reduced functional connectivity within SMN in schizophrenia patients compared to controls and SMN hypoconnectivity with the cerebellum in schizophrenia patients. The latter was strongly correlated with the severity of alogia, one of the main psychotic symptoms, i.e. poverty of speech and reduction in spontaneous speech,. Our results are consistent with the recent knowledge about the role of the cerebellum in cognitive functioning and its abnormalities in psychiatric disorders, e.g. schizophrenia. In conclusion, the presented results, for the first time clearly showed the involvement of the cerebellum hypoconnectivity with SMN in the persistence and severity of alogia symptoms in schizophrenia.

Individual patterns of functional connectivity in neonates as revealed by surface-based Bayesian modeling

Resting-state functional connectivity is a widely used approach to study the functional brain network organization during early brain development. However, the estimation of functional connectivity networks in individual infants has been rather elusive due to the unique challenges involved with functional magnetic resonance imaging (fMRI) data from young populations. Here, we use fMRI data from the developing Human Connectome Project (dHCP) database to characterize individual variability in a large cohort of term-born infants (N = 289) using a novel data-driven Bayesian framework. To enhance alignment across individuals, the analysis was conducted exclusively on the cortical surface, employing surface-based registration guided by age-matched neonatal atlases. Using 10 minutes of resting-state fMRI data, we successfully estimated subject-level maps for fourteen brain networks/subnetworks along with individual functional parcellation maps that revealed differences between subjects. We also found a significant relationship between age and mean connectivity strength in all brain regions, including previously unreported findings in higher-order networks. These results illustrate the advantages of surface-based methods and Bayesian statistical approaches in uncovering individual variability within very young populations.

A standardized image processing and data quality platform for rodent fMRI

Functional magnetic resonance imaging in rodents holds great potential for advancing our understanding of brain networks. Unlike the human community, there remains no standardized resource in rodents for image processing, analysis and quality control, posing significant reproducibility limitations. Our software platform, Rodent Automated Bold Improvement of EPI Sequences, is a pipeline designed to address these limitations for preprocessing, quality control, and confound correction, along with best practices for reproducibility and transparency. We demonstrate the robustness of the preprocessing workflow by validating performance across multiple acquisition sites and both mouse and rat data. Building upon a thorough investigation into data quality metrics across acquisition sites, we introduce guidelines for the quality control of network analysis and offer recommendations for addressing issues. Taken together, this software platform will allow the emerging community to adopt reproducible practices and foster progress in translational neuroscience.

Greater ventral striatal functional connectivity in cigarette smokers relative to non-smokers across a spectrum of alcohol consumption

Cigarette smoking is associated with elevated risk of disease and mortality and contributes to heavy healthcare-related economic burdens. The nucleus accumbens is implicated in numerous reward-related behaviors, including reinforcement learning and incentive salience. The established functional connectivity of the accumbens includes regions associated with motivation, valuation, and affective processing. Although the high comorbidity of cigarette smoking with drinking behaviors may collectively affect brain activity, there could be independent effects of smoking in alcohol use disorder that impact brain function and behavior. We hypothesized that smoking status, independent of alcohol use, would be associated with aberrations of nucleus accumbens functional connectivity to brain regions that facilitate reward processing, salience attribution, and inhibitory control. Resting state functional magnetic resonance imaging data from thirty-one nonsmokers and nineteen smoking individuals were analyzed using seed-based correlations of the bilateral accumbens with all other brain voxels. Statistical models accounted for drinks consumed per week. The smoking group demonstrated significantly higher functional connectivity between the left accumbens and the bilateral insula and anterior cingulate cortex, as well as hyperconnectivity between the right accumbens and the insula. Confirmatory analyses using the insula and cingulate clusters generated from the original analysis as seed regions reproduced the hyperconnectivity in smokers between the bilateral insular regions and the accumbens. In conclusion, smoking status had distinct effects on neural activity; hyperconnectivity between the accumbens and insula in smokers may reflect enhanced encoding of the reinforcing effects of smoking and greater orientation toward smoking-associated stimuli.

Mapping the neurodevelopmental predictors of psychopathology

Neuroimaging research has uncovered a multitude of neural abnormalities associated with psychopathology, but few prediction-based studies have been conducted during adolescence, and even fewer used neurobiological features that were extracted across multiple neuroimaging modalities. This gap in the literature is critical, as deriving accurate brain-based models of psychopathology is an essential step towards understanding key neural mechanisms and identifying high-risk individuals. As such, we trained adaptive tree-boosting algorithms on multimodal neuroimaging features from the Lifespan Human Connectome Developmental (HCP-D) sample that contained 956 participants between the ages of 8 to 22 years old. Our feature space consisted of 1037 anatomical, 1090 functional, and 192 diffusion MRI features, which were used to derive models that separately predicted internalizing symptoms, externalizing symptoms, and the general psychopathology factor. We found that multimodal models were the most accurate, but all brain-based models of psychopathology yielded out-of-sample predictions that were weakly correlated with actual symptoms (r2 < 0.15). White matter microstructural properties, including orientation dispersion indices and intracellular volume fractions, were the most predictive of general psychopathology, followed by cortical thickness and functional connectivity. Spatially, the most predictive features of general psychopathology were primarily localized within the default mode and dorsal attention networks. These results were mostly consistent across all dimensions of psychopathology, except orientation dispersion indices and the default mode network were not as heavily weighted in the prediction of internalizing and externalizing symptoms. Taken with prior literature, it appears that neurobiological features are an important part of the equation for predicting psychopathology but relying exclusively on neural markers is clearly not sufficient, especially among adolescent samples with subclinical symptoms. Consequently, risk factor models of psychopathology may benefit from incorporating additional sources of information that have also been shown to explain individual differences, such as psychosocial factors, environmental stressors, and genetic vulnerabilities.

Association between patent foramen ovale and migraine: evidence from a resting-state fMRI study

A relationship between migraine without aura (MO) and patent foramen ovale (PFO) has been observed, but the neural basis underlying this relationship remains elusive. Utilizing independent component analysis via functional magnetic resonance imaging, we examined functional connectivity (FC) within and across networks in 146 patients with MO (75 patients with and 71 patients without PFO) and 70 healthy controls (35 patients each with and without PFO) to elucidate the individual effects of MO and PFO, as well as their interaction, on brain functional networks. The main effect of PFO manifested exclusively in the FC among the visual, auditory, default mode, dorsal attention and salience networks. Furthermore, the interaction effect between MO and PFO was discerned in brain clusters of the left frontoparietal network and lingual gyrus network, as well as the internetwork FC between the left frontoparietal network and the default mode network (DMN), the occipital pole and medial visual networks, and the dorsal attention and salience networks. Our findings suggest that the presence of a PFO shunt in patients with MO is accompanied by various FC changes within and across networks. These changes elucidate the intricate mechanisms linked to PFO-associated migraines and provide a basis for identifying novel noninvasive biomarkers.

Functional connectivity associations with menstrual pain characteristics in adolescents: an investigation of the triple network model

ABSTRACT

Menstrual pain is associated with deficits in central pain processing, yet neuroimaging studies to date have all been limited by focusing on group comparisons of adult women with vs without menstrual pain. This study aimed to investigate the role of the triple network model (TNM) of brain networks in adolescent girls with varied menstrual pain severity ratings. One hundred participants (ages 13-19 years) completed a 6-min resting state functional magnetic resonance imaging (fMRI) scan and rated menstrual pain severity, menstrual pain interference, and cumulative menstrual pain exposure. Imaging analyses included age and gynecological age (years since menarche) as covariates. Menstrual pain severity was positively associated with functional connectivity between the cingulo-opercular salience network (cSN) and the sensory processing regions, limbic regions, and insula, and was also positively associated with connectivity between the left central executive network (CEN) and posterior regions. Menstrual pain interference was positively associated with connectivity between the cSN and widespread brain areas. In addition, menstrual pain interference was positively associated with connectivity within the left CEN, whereas connectivity both within the right CEN and between the right CEN and cortical areas outside the network (including the insula) were negatively associated with menstrual pain interference. Cumulative menstrual pain exposure shared a strong negative association with connectivity between the default mode network and other widespread regions associated with large-scale brain networks. These findings support a key role for the involvement of TNM brain networks in menstrual pain characteristics and suggest that alterations in pain processing exist in adolescents with varying levels of menstrual pain.

Hippocampal connectivity patterns echo macroscale cortical evolution in the primate brain

While the hippocampus is key for human cognitive abilities, it is also a phylogenetically old cortex and paradoxically considered evolutionarily preserved. Here, we introduce a comparative framework to quantify preservation and reconfiguration of hippocampal organisation in primate evolution, by analysing the hippocampus as an unfolded cortical surface that is geometrically matched across species. Our findings revealed an overall conservation of hippocampal macro- and micro-structure, which shows anterior-posterior and, perpendicularly, subfield-related organisational axes in both humans and macaques. However, while functional organisation in both species followed an anterior-posterior axis, we observed a marked reconfiguration in the latter across species, which mirrors a rudimentary integration of the default-mode-network in non-human primates. Here we show that microstructurally preserved regions like the hippocampus may still undergo functional reconfiguration in primate evolution, due to their embedding within heteromodal association networks.

Brain state dynamics differ between eyes open and eyes closed rest

The human brain exhibits spatio-temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) eyes open (EO) and eyes closed (EC) resting-state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG-defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window-based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting-state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha-BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in-depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.

Evaluating functional connectivity differences between DBS ON/OFF states in essential tremor

Deep brain stimulation (DBS) targeting the ventral intermediate (Vim) nucleus of the thalamus is an effective treatment for essential tremor (ET). We studied 15 ​ET patients undergoing DBS to a major input/output tract of the Vim, the dentato-rubro-thalamic tract (DRTt), using resting state functional MRI (rsfMRI) to evaluate connectivity differences between DBS ON and OFF and elucidate significant regions most influential in impacting tremor control and/or concomitant gait ataxia. Anatomical/functional 1.5T MRIs were acquired and replicated for each DBS state. Tremor severity and gait ataxia severity were scored with DBS ON at optimal stimulation parameters and immediately upon DBS OFF. Whole brain analysis was performed using dual regression analysis followed by randomized permutation testing for multiple correction comparison. Regions of interest (ROI) analysis was also performed. All 15 patients had tremor improvement between DBS ON/OFF (p ​< ​0.001). Whole brain analysis revealed significant connectivity changes between states in the left pre-central gyrus and left supplemental motor area. Group analysis of ROIs revealed that, with threshold p ​< ​0.05, in DBS ON vs. OFF both tremor duration and tremor improvement were significantly correlated to changes in connectivity. A sub-group analysis of patients with greater ataxia had significantly decreased functional connectivity between multiple ROIs in the cortex and cerebellum when DBS was ON compared to OFF. Stimulation of the DRTt and concordant improvement of tremor resulted in connectivity changes seen in multiple regions outside the motor network; when combined with both structural and electrophysiologic connectivity, this may help to serve as a biomarker to improve DBS targeting and possibly predict outcome.

Association of Lower Rostral Anterior Cingulate GABA+ and Dysregulated Cortisol Stress Response With Altered Functional Connectivity in Young Adults With Lifetime Depression: A Multimodal Imaging Investigation of Trait and State Effects

OBJECTIVE

Preclinical work suggests that excess glucocorticoids and reduced cortical γ-aminobutyric acid (GABA) may affect sex-dependent differences in brain regions implicated in stress regulation and depressive phenotypes. The authors sought to address a critical gap in knowledge, namely, how stress circuitry is functionally affected by glucocorticoids and GABA in current or remitted major depressive disorder (MDD).

METHODS

Multimodal imaging data were collected from 130 young adults (ages 18-25), of whom 44 had current MDD, 42 had remitted MDD, and 44 were healthy comparison subjects. GABA+ (γ-aminobutyric acid and macromolecules) was assessed using magnetic resonance spectroscopy, and task-related functional MRI data were collected under acute stress and analyzed using data-driven network modeling.

RESULTS

Across modalities, trait-related abnormalities emerged. Relative to healthy comparison subjects, both clinical groups were characterized by lower rostral anterior cingulate cortex (rACC) GABA+ and frontoparietal network amplitude but higher amplitude in salience and stress-related networks. For the remitted MDD group, differences from the healthy comparison group emerged in the context of elevated cortisol levels, whereas the MDD group had lower cortisol levels than the healthy comparison group. In the comparison group, frontoparietal and stress-related network connectivity was positively associated with cortisol level (highlighting putative top-down regulation of stress), but the opposite relationship emerged in the MDD and remitted MDD groups. Finally, rACC GABA+ was associated with stress-induced changes in connectivity between overlapping default mode and salience networks.

CONCLUSIONS

Lifetime MDD was characterized by reduced rACC GABA+ as well as dysregulated cortisol-related interactions between top-down control (frontoparietal) and threat (task-related) networks. These findings warrant further investigation of the role of GABA in the vulnerability to and treatment of MDD.

Neurobiological correlates of religious coping among older adults with and without mood disorders: An exploratory study

In this study, 32 older adults with and without mood disorders completed resting-state functional Magnetic Resonance Imaging and measures of demographics, spirituality/religion, positive and negative religious coping, and depression. Group Independent Component Analysis identified and selected three a priori resting state networks [cingulo-opercular salience (cSN), central executive (CEN) and Default Mode Networks (DMN)] within the Triple Network Mode. We investigated associations of religious coping with within- and between-network connectivity, controlling for age. Insular connectivity within the cSN was associated with negative religious coping. Religious coping was associated with anti-correlation between the DMN and CEN even when controlling for depression.

Correlation of altered intrinsic functional connectivity with impaired self-regulation in children and adolescents with ADHD

BACKGROUND

Attention-deficit hyperactivity disorder (ADHD) has a high prevalence of co-occurring impaired self-regulation (dysregulation), exacerbating adverse outcomes. Neural correlates underlying impaired self-regulation in ADHD remain inconclusive. We aimed to investigate the impact of dysregulation on intrinsic functional connectivity (iFC) in children with ADHD and the correlation of iFC with dysregulation among children with ADHD relative to typically developing controls (TDC).

METHODS

Resting-state functional MRI data of 71 children with ADHD (11.38 ± 2.44 years) and 117 age-matched TDC were used in the final analysis. We restricted our analyses to resting-state networks (RSNs) of interest derived from independent component analysis. Impaired self-regulation was estimated based on the Child Behavioral Checklist-Dysregulation Profile.

RESULTS

Children with ADHD showed stronger iFC than TDC in the left frontoparietal network, somatomotor network (SMN), visual network (VIS), default-mode network (DMN), and dorsal attention network (DAN) (FWE-corrected alpha < 0.05). After adding dysregulation levels as an extra regressor, the ADHD group only showed stronger iFC in the VIS and SMN. ADHD children with high dysregulation had higher precuneus iFC within DMN than ADHD children with low dysregulation. Angular gyrus iFC within DMN was positively correlated with dysregulation in the ADHD group but negatively correlated with dysregulation in the TDC group. Functional network connectivity showed ADHD had a greater DMN-DAN connection than TDC, regardless of the dysregulation level.

CONCLUSIONS

Our findings suggest that DMN connectivity may contribute to impaired self-regulation in ADHD. Impaired self-regulation should be considered categorical and dimensional moderators for the neural correlates of altered iFC in ADHD.

Depressed patients with childhood maltreatment display altered intra- and inter-network resting state functional connectivity

BACKGROUND

Childhood maltreatment (CM) is a major risk factor for the development of major depressive disorder (MDD). To gain more knowledge on how adverse childhood experiences influence the development of brain architecture, we studied functional connectivity (FC) alterations of neural networks of depressed patients with, or without the history of CM.

METHODS

Depressed patients with severe childhood maltreatment (n = 18), MDD patients without maltreatment (n = 19), and matched healthy controls (n = 20) were examined with resting state functional MRI. History of maltreatment was assessed with the 28-item Childhood Trauma Questionnaire. Intra- and inter-network FC alterations were evaluated using FMRIB Software Library and CONN toolbox.

RESULTS

We found numerous intra- and inter-network FC alterations between the maltreated and the non-maltreated patients. Intra-network FC differences were found in the default mode, visual and auditory networks, and cerebellum. Network modelling revealed several inter-network FC alterations connecting the default mode network with the executive control, salience and cerebellar networks. Increased inter-network FC was found in maltreated patients between the sensory-motor and visual, cerebellar, default mode and salience networks.

LIMITATIONS

Relatively small sample size, cross-sectional design, and retrospective self-report questionnaire to assess adverse childhood experiences.

CONCLUSIONS

Our findings confirm that severely maltreated depressed patients display numerous alterations of intra- and inter-network FC strengths, not only in their fronto-limbic circuits, but also in sensory-motor, visual, auditory, and cerebellar networks. These functional alterations may explain that maltreated individuals typically display altered perception and are prone to develop functional neurological symptom disorder (conversion disorder) in adulthood.

Clavulanic Acid-Mediated Increases in Anterior Cingulate Glutamate Levels are Associated With Decreased Cocaine Craving and Brain Network Functional Connectivity Changes

Background

There is an urgent need for pharmacological treatment for cocaine (COC) use disorder (CUD). Glutamatergic transmission in the prefrontal cortex is affected by addictive behaviors. Clavulanic acid (CLAV), a glutamate transporter GLT-1 (excitatory amino acid transporter) activator, is a clinical-stage medication that has potential for treating CUD.

Methods

In a pilot study, nine participants with CUD received 500 mg CLAV with dose escalations to 750 mg and 1000 mg over 10 days. In 5 separate magnetic resonance imaging (MRI) sessions, brain anterior cingulate cortex (ACC) glutamate level and resting state network (RSN) functional connectivity (FC) were assessed using MR spectroscopy and functional MRI. Craving was assessed at the same time points, between baseline (before CLAV), 6 days, and 10 days of CLAV. Independent component analysis with dual regression was used to identify RSN FC changes from baseline to Days 6 and 10. Relationships among glutamate, craving, and resting state FC values were analyzed.

Results

Participants who achieved high ACC glutamate levels after CLAV treatment had robust decreases in COC craving (r = -0.90, P = 0.0009, n = 9). The salience network (SN) and executive control network (ECN) demonstrated an association between increased FC after CLAV treatment and low baseline ACC Glu levels (SN CLAV 750 mg, r = -0.82, P = 0.007) (ECN CLAV 1000 mg, r = -0.667, P = 0.050; n = 9).

Conclusions

Glutamate associated changes in craving and FC of the salience and executive control brain networks support CLAV as a potentially efficacious pharmacological treatment for CUD.

Resting-state networks and anosognosia in Alzheimer's disease

Background

Recent evidence suggests that anosognosia or unawareness of cognitive impairment in Alzheimer's Disease (AD) may be explained by a disconnection between brain regions involved in accessing and monitoring information regarding self and others. It has been demonstrated that AD patients with anosognosia have reduced connectivity within the default mode network (DMN) and that anosognosia in people with prodromal AD is positively associated with bilateral anterior cingulate cortex (ACC), suggesting a possible role of this region in mechanisms of awareness in the early phase of disease. We hypothesized that anosognosia in AD is associated with an imbalance between the activity of large-scale resting-state functional magnetic resonance imaging (fMRI) networks, in particular the DMN, the salience network (SN), and the frontoparietal network (FPN).

Methods

Sixty patients with MCI and AD dementia underwent fMRI and neuropsychological assessment including the Anosognosia Questionnaire Dementia (AQ-D), a measure of anosognosia based on a discrepancy score between patient's and carer's judgments. After having applied Independent Component Analysis (ICA) to resting fMRI data we performed: (i) correlations between the AQ-D score and functional connectivity in the DMN, SN, and FPN, and (ii) comparisons between aware and unaware patients of the DMN, SN, and FPN functional connectivity.

Results

We found that anosognosia was associated with (i) weak functional connectivity within the DMN, in posterior and middle cingulate cortex particularly, (ii) strong functional connectivity within the SN in ACC, and between the SN and basal ganglia, and (iii) a heterogenous effect concerning the functional connectivity of the FPN, with a weak connectivity between the FPN and PCC, and a strong connectivity between the FPN and ACC. The observed effects were controlled for differences in severity of cognitive impairment and age.

Conclusion

Anosognosia in the AD continuum is associated with a dysregulation of the functional connectivity of three large-scale networks, namely the DMN, SN, and FPN.

Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics

Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.

Resting-state functional magnetic resonance imaging reveals functional connectivity alteration in the experimental autoimmune encephalomyelitis model of multiple sclerosis

Multiple sclerosis (MS) is an autoimmune degenerative disease targeting white matter in the central nervous system. The most common animal model that mimics MS is experimental autoimmune encephalomyelitis (EAE) and it plays a crucial role in pharmacological research, from the identification of a therapeutic target to the in vivo validation of efficacy. Magnetic resonance imaging (MRI) is largely used to detect MS lesions, and resting-state functional MRI (rsfMRI) to investigate alterations in the brain functional connectivity (FC). MRI was mainly used in EAE studies to detect lesions in the spinal cord and brain. The current longitudinal MRI study aims to validate rsfMRI as a biomarker of the disease progression in the myelin oligodendrocyte glycoprotein 35-55 induced EAE animal model of MS. MR images were acquired 14, 25, and 50 days postimmunization. Seed-based analysis was used to investigate the whole-brain FC with some predefined areas, such as the thalamic regions, cerebellum, motor and somatosensory cortex. When compared with the control group, the EAE group exhibited a slightly altered FC and a decreasing trend in the total number of activated voxels along the disease progression. The most interesting result regards the whole-brain FC with the cerebellum. A hyperconnectivity behavior was found at an early phase and a significant reduced connectivity at a late phase. Moreover, we found a negative correlation between the total number of activated voxels during the late phase and the cumulative disease index. The results obtained provide a clinically relevant experimental platform that may be pivotal for the elucidation of the key mechanisms of accumulation of irreversible disability, as well as the development of innovative therapies for MS. Moreover, the negative correlation between the disease severity and the size of the activated area suggests a possible research pathway to follow for the resolution of the clinico-radiological paradox.

Resting state networks of awake adolescent and adult squirrel monkeys using ultra-high field (9.4T) functional magnetic resonance imaging

Resting state networks (RSNs) are increasingly forwarded as candidate biomarkers for neuropsychiatric disorders. Such biomarkers may provide objective measures for evaluating novel therapeutic interventions in nonhuman primates often used in translational neuroimaging research. This study aimed to characterize the RSNs of awake squirrel monkeys and compare the characteristics of those networks in adolescent and adult subjects. Twenty-seven squirrel monkeys (n=12 adolescents [6 male/6 female] ∼2.5 years and n=15 adults [7 male/8 female] ∼9.5 years) were gradually acclimated to awake scanning procedures; whole-brain fMRI images were acquired with a 9.4 Tesla scanner. Group level independent component (ICA) analysis (30 ICs) with dual regression was used to detect and compare RSNs. Twenty ICs corresponding to physiologically meaningful networks representing a range of neural functions, including motor, sensory, reward, and cognitive processes were identified in both adolescent and adult monkeys. The reproducibility of these RSNs was evaluated across several ICA model orders. Adults showed a trend for greater connectivity compared to adolescent subjects in two of the networks of interest: (1) in the right occipital region with the OFC network and (2) in the left temporal cortex, bilateral occipital cortex, and cerebellum with the posterior cingulate network. However, when age was entered into the above model, this trend for significance was lost. These results demonstrate that squirrel monkey RSNs are stable and consistent with RSNs previously identified in humans, rodents, and other nonhuman primate species. These data also identify several networks in adolescence that are conserved and others that may change into adulthood.Significance Statement Functional magnetic resonance imaging procedures have revealed important information about how the brain is modified by experimental manipulations, disease states, and aging throughout the lifespan. Preclinical neuroimaging, especially in nonhuman primates, has become a frequently used means to answer targeted questions related to brain resting-state functional connectivity. The present study characterized resting state networks (RSNs) in adult and adolescent squirrel monkeys; twenty RSNs corresponding to networks representing a range of neural functions were identified. The RSNs identified here can be utilized in future studies examining the effects of experimental manipulations on brain connectivity in squirrel monkeys. These data also may be useful for comparative analysis with other primate species to provide an evolutionary perspective for understanding brain function and organization.

Opaque Ontology: Neuroimaging Classification of ICD-10 Diagnostic Groups in the UK Biobank

Background

1.The use of machine learning to classify diagnostic cases versus controls defined based on diagnostic ontologies such as the ICD-10 from neuroimaging features is now commonplace across a wide range of diagnostic fields. However, transdiagnostic comparisons of such classifications are lacking. Such transdiagnostic comparisons are important to establish the specificity of classification models, set benchmarks, and assess the value of diagnostic ontologies.

Results

2.We investigated case-control classification accuracy in 17 different ICD-10 diagnostic groups from Chapter V (mental and behavioral disorders) and Chapter VI (diseases of the nervous system) using data from the UK Biobank. Classification models were trained using either neuroimaging (structural or functional brain MRI feature sets) or socio-demographic features. Random forest classification models were adopted using rigorous shuffle splits to estimate stability as well as accuracy of case-control classifications. Diagnostic classification accuracies were benchmarked against age classification (oldest versus youngest) from the same feature sets and against additional classifier types (K-nearest neighbors and linear support vector machine). In contrast to age classification accuracy, which was high for all feature sets, few ICD-10 diagnostic groups were classified significantly above chance (namely, demyelinating diseases based on structural neuroimaging features, and depression based on socio-demographic and functional neuroimaging features).

Conclusion

3.These findings highlight challenges with the current disease classification system, leading us to recommend caution with the use of ICD-10 diagnostic groups as target labels in brain-based disease prediction studies.

Impact of the day/night cycle on functional connectome in ageing male and female mice

To elucidate how time of day, sex, and age affect functional connectivity (FC) in mice, we aimed to examine whether the mouse functional connectome varied with the day/night cycle and whether it depended on sex and age. We explored C57Bl6/J mice (6♀ and 6♂) at mature age (5 ± 1 months) and middle-age (14 ± 1 months). Each mouse underwent Blood Oxygen-Level-Dependent (BOLD) resting-state functional MRI (rs-fMRI) on a 7T scanner at four different times of the day, two under the light condition and two under the dark condition. Data processing consisted of group independent component analysis (ICA) and region-level analysis using resting-state networks (RSNs) derived from literature. Linear mixed-effect models (LMEM) were used to assess the effects of sex, lighting condition and their interactions for each RSN obtained with group-ICA (RSNs-GICA) and six bilateral RSNs adapted from literature (RSNs-LIT). Our study highlighted new RSNs in mice related to day/night alternation in addition to other networks already reported in the literature. In mature mice, we found sex-related differences in brain activation only in one RSNs-GICA comprising the cortical, hippocampal, midbrain and cerebellar regions of the right hemisphere. In males, brain activity was significantly higher in the left hippocampus, the retrosplenial cortex, the superior colliculus, and the cerebellum regardless of lighting condition; consistent with the role of these structures in memory formation and integration, sleep, and sex-differences in memory processing. Experimental constraints limited the analysis to the impact of light/dark cycle on the RSNs for middle-aged females. We detected significant activation in the pineal gland during the dark condition, a finding in line with the nocturnal activity of this gland. For the analysis of RSNs-LIT, new variables "sexage" (sex and age combined) and "edges" (pairs of RSNs) were introduced. FC was calculated as the Pearson correlation between two RSNs. LMEM revealed no effect of sexage or lighting condition. The FC depended on the edges, but there were no interaction effects between sexage, lighting condition and edges. Interaction effects were detected between i) sex and lighting condition, with higher FC in males under the dark condition, ii) sexage and edges with higher FC in male brain regions related to vision, memory, and motor action. We conclude that time of day and sex should be taken into account when designing, analyzing, and interpreting functional imaging studies in rodents.

Value Representations of Spite Sensitivity in Psychosis on the Minnesota Trust Game

BACKGROUND

Spite sensitivity provides a valuable construct to understand persecutory ideation and its underlying neural mechanisms. We examined the relationship between persecution and spite sensitivity in psychosis to identify their neural substrates.

METHODS

In a 3T magnetic resonance imaging scanner, 49 participants with psychosis played the Minnesota Trust Game, in which they decided whether to take a small amount of money or trust a partner to choose between fair and unfair distributions of money. In some conditions, the partner benefited from the unfair option, while in others, the partner lost money. Participants who were untrusting in the second condition (suspiciousness) showed heightened sensitivity to spite. Behavioral measures included mistrust during the 2 conditions of the game, which were compared with Brief Psychiatric Rating Scale persecution and computational modeling. Functional connectivity and blood oxygen level-dependent analyses were also conducted on a priori regions during spite-sensitive decisions.

RESULTS

Behavioral results replicated previous findings; participants who experienced more persecutory ideation trusted less, specifically in the suspiciousness condition. Functional connectivity findings showed that decreased connectivity between the orbitofrontal cortex-insula and the left frontoparietal network was associated with increased persecutory ideation and estimated spite-guilt (a marker of spite sensitivity). Additionally, we found differences between conditions in caudate nucleus, medial prefrontal cortex, and lateral orbitofrontal cortex activation.

CONCLUSIONS

These findings provide a new perspective on the origin of positive symptoms by identifying primary brain circuits that are related to both spite sensitivity and persecutory ideation.

Altered resting-state functional connectivity and dynamic network properties in cognitive impairment: an independent component and dominant-coactivation pattern analyses study

Introduction

Cognitive impairment (CI) due to Alzheimer's disease (AD) encompasses a decline in cognitive abilities and can significantly impact an individual's quality of life. Early detection and intervention are crucial in managing CI, both in the preclinical and prodromal stages of AD prior to dementia.

Methods

In this preliminary study, we investigated differences in resting-state functional connectivity and dynamic network properties between 23 individual with CI due to AD based on clinical assessment and 15 healthy controls (HC) using Independent Component Analysis (ICA) and Dominant-Coactivation Pattern (d-CAP) analysis. The cognitive status of the two groups was also compared, and correlations between cognitive scores and d-CAP switching probability were examined.

Results

Results showed comparable numbers of d-CAPs in the Default Mode Network (DMN), Executive Control Network (ECN), and Frontoparietal Network (FPN) between HC and CI groups. However, the Visual Network (VN) exhibited fewer d-CAPs in the CI group, suggesting altered dynamic properties of this network for the CI group. Additionally, ICA revealed significant connectivity differences for all networks. Spatial maps and effect size analyses indicated increased coactivation and more synchronized activity within the DMN in HC compared to CI. Furthermore, reduced switching probabilities were observed for the CI group in DMN, VN, and FPN networks, indicating less dynamic and flexible functional interactions.

Discussion

The findings highlight altered connectivity patterns within the DMN, VN, ECN, and FPN, suggesting the involvement of multiple functional networks in CI. Understanding these brain processes may contribute to developing targeted diagnostic and therapeutic strategies for CI due to AD.

Reduced default mode network effective connectivity in healthy aging is modulated by years of education

Aging is a major risk factor for neurodegenerative diseases like dementia and Alzheimer's disease. Even in non-pathological aging, decline in cognitive functioning is observed in the majority of the elderly population, necessitating the importance of studying the processes involved in healthy aging in order to identify brain biomarkers that promote the conservation of functioning. The default mode network (DMN) has been of special interest to aging research due to its vulnerability to atrophy and functional decline over the course of aging. Prior work has focused almost exclusively on functional (i.e. undirected) connectivity, yet converging findings are scarce. Therefore, we set out to use spectral dynamic causal modeling to investigate changes in the effective (i.e. directed) connectivity within the DMN and to discover changes in information flow in a sample of cognitively normal adults spanning from 48 to 89 years (n = 63). Age was associated to reduced verbal memory performance. Modeling of effective connectivity revealed a pattern of age-related downregulation of posterior DMN regions driven by inhibitory connections from the hippocampus and middle temporal gyrus. Additionally, there was an observed decline in the hippocampus' susceptibility to network inputs with age, effectively disconnecting itself from other regions. The estimated effective connectivity parameters were robust and able to predict the age in out of sample estimates in a leave-one-out cross-validation. Attained education moderated the effects of aging, largely reversing the observed pattern of inhibitory connectivity. Thus, medial prefrontal cortex, hippocampus and posterior DMN regions formed an excitatory cycle of extrinsic connections related to the interaction of age and education. This suggests a compensatory role of years of education in effective connectivity, stressing a possible target for interventions. Our findings suggest a connection to the concept of cognitive reserve, which attributes a protective effect of educational level on cognitive decline in aging (Stern, 2009).

Distinct functional subnetworks of cognitive domains in older adults with minor cognitive deficits

Although past research has established a relationship between functional connectivity and cognitive function, less is known about which cognitive domains are associated with which specific functional networks. This study investigated associations between functional connectivity and global cognitive function and performance in the domains of memory, executive function and psychomotor speed in 166 older adults aged 75-91 years (mean = 80.3 ± 3.8) with minor cognitive deficits (Mini-Mental State Examination scores between 21 and 27). Functional connectivity was assessed within 10 standard large-scale resting-state networks and on a finer spatial resolution between 300 nodes in a functional connectivity matrix. No domain-specific associations with mean functional connectivity within large-scale resting-state networks were found. Node-level analysis revealed that associations between functional connectivity and cognitive performance differed across cognitive functions in strength, location and direction. Specific subnetworks of functional connections were found for each cognitive domain in which higher connectivity between some nodes but lower connectivity between other nodes were related to better cognitive performance. Our findings add to a growing body of literature showing differential sensitivity of functional connections to specific cognitive functions and may be a valuable resource for hypothesis generation of future studies aiming to investigate specific cognitive dysfunction with resting-state functional connectivity in people with beginning cognitive deficits.

Intrinsic Functional Connectivity between the Anterior Insular and Retrosplenial Cortex as a Moderator and Consequence of Cocaine Self-Administration in Rats

While functional brain imaging studies in humans suggest that chronic cocaine use alters functional connectivity (FC) within and between key large-scale brain networks, including the default mode network (DMN), the salience network (SN), and the central executive network (CEN), cross-sectional studies in humans are challenging to obtain brain FC prior to cocaine use. Such information is critical to reveal the relationship between individual's brain FC and the subsequent development of cocaine dependence and brain changes during abstinence. Here, we performed a longitudinal study examining functional magnetic resonance imaging (fMRI) data in male rats (n = 7), acquired before cocaine self-administration (baseline), on 1 d of abstinence following 10 d of cocaine self-administration, and again after 30 d of experimenter-imposed abstinence. Using repeated-measures analysis of variance (ANOVA) with network-based statistics (NBS), significant connectivity changes were found between anterior insular cortex (AI) of the SN, retrosplenial cortex (RSC) of the DMN, somatosensory cortex, and caudate-putamen (CPu), with AI-RSC FC showing the most robust changes between baseline and 1 d of abstinence. Additionally, the level of escalated cocaine intake is associated with AI-RSC and AI-CPu FC changes between 1 d and 30 d of abstinence; further, the subjects' AI-RSC FC prior to cocaine intake is a significant moderator for the AI-RSC changes during abstinence. These results provide novel insights into the roles of AI-RSC FC before and after cocaine intake and suggest this circuit to be a potential target to modulate large-scale network and associated behavioral changes in cocaine use disorders.

Prenatal Exposure to Maternal Mood Entropy Is Associated With a Weakened and Inflexible Salience Network in Adolescence

BACKGROUND

Fetal exposure to maternal mood dysregulation influences child cognitive and emotional development, which may have long-lasting implications for mental health. However, the neurobiological alterations associated with this dimension of adversity have yet to be explored. Here, we tested the hypothesis that fetal exposure to entropy, a novel index of dysregulated maternal mood, would predict the integrity of the salience network, which is involved in emotional processing.

METHODS

A sample of 138 child-mother pairs (70 females) participated in this prospective longitudinal study. Maternal negative mood level and entropy (an index of variable and unpredictable mood) were assessed 5 times during pregnancy. Adolescents engaged in a functional magnetic resonance imaging task that was acquired between 2 resting-state scans. Changes in network integrity were analyzed using mixed-effect and latent growth curve models. The amplitude of low frequency fluctuations was analyzed to corroborate findings.

RESULTS

Prenatal maternal mood entropy, but not mood level, was associated with salience network integrity. Both prenatal negative mood level and entropy were associated with the amplitude of low frequency fluctuations of the salience network. Latent class analysis yielded 2 profiles based on changes in network integrity across all functional magnetic resonance imaging sequences. The profile that exhibited little variation in network connectivity (i.e., inflexibility) consisted of adolescents who were exposed to higher negative maternal mood levels and more entropy.

CONCLUSIONS

These findings suggest that fetal exposure to maternal mood dysregulation is associated with a weakened and inflexible salience network. More broadly, they identify maternal mood entropy as a novel marker of early adversity that exhibits long-lasting associations with offspring brain development.

Measures of resting-state brain network segregation and integration vary in relation to data quantity: implications for within and between subject comparisons of functional brain network organization

Measures of functional brain network segregation and integration vary with an individual's age, cognitive ability, and health status. Based on these relationships, these measures are frequently examined to study and quantify large-scale patterns of network organization in both basic and applied research settings. However, there is limited information on the stability and reliability of the network measures as applied to functional time-series; these measurement properties are critical to understand if the measures are to be used for individualized characterization of brain networks. We examine measurement reliability using several human datasets (Midnight Scan Club and Human Connectome Project [both Young Adult and Aging]). These datasets include participants with multiple scanning sessions, and collectively include individuals spanning a broad age range of the adult lifespan. The measurement and reliability of measures of resting-state network segregation and integration vary in relation to data quantity for a given participant's scan session; notably, both properties asymptote when estimated using adequate amounts of clean data. We demonstrate how this source of variability can systematically bias interpretation of differences and changes in brain network organization if appropriate safeguards are not included. These observations have important implications for cross-sectional, longitudinal, and interventional comparisons of functional brain network organization.

Improved cognition after rifaximin treatment is associated with changes in intra- and inter-brain network functional connectivity

BACKGROUND

Rifaximin is a non-reabsorbable antibiotic which acts at gut level, and improves cognition and inflammatory parameters in minimal hepatic encephalopathy (MHE) patients, but not all patients show the same level of response. This study aims to assess brain activity, both within and between brain networks, following rifaximin treatment, considering the differences between response groups as well.

METHODS

Twenty-two healthy controls and 53 patients with cirrhosis (22 without and 31 with MHE, diagnosed by Psychometric Hepatic Encephalopathy Score, PHES) performed psychometric, attention and coordination tests, and blood inflammatory parameters were measured. Resting-state functional magnetic resonance imaging (fMRI) acquisitions were performed on controls and MHE patients. Eighteen MHE patients underwent a rifaximin treatment for 6 months, after which all measures were repeated. fMRI images were analysed and changes after treatment were assessed.

RESULTS

After rifaximin treatment, 13 patients improved their PHES score (Responder patients) while 5 did not (Non-responder patients). No significant decrease in blood ammonia was observed after rifaximin treatment, but there was a decrease in plasma inflammatory cytokines in responder patients. A global effect of rifaximin was detected on the sensorimotor and fronto-parietal networks. Responder patients showed a relative increase of thalamic network connectivity in comparison to non-responder patients. Before treatment, responder and non-responder patients showed connectivity differences in basal ganglia network. The connection of the sensorimotor and thalamic networks between them and with other networks suffered changes after treatment. These connections between networks mostly decreased after treatment. All changes and differences showed a significant level of correlation with the performance of psychometric tests and the blood levels of inflammatory biomarkers.

CONCLUSIONS

There was an improvement of the communication between executive, motor and attention-related brain areas, and their functional independence following rifaximin treatment. Patients who respond also show a less deteriorated connection involved in these functions before treatment. Results suggest that the improved inflammatory state of MHE patients, following rifaximin treatment would favour the observed changes in brain function and enhanced cognitive performance.

Comprehensive Separation Algorithm for Single-Channel Signals Based on Symplectic Geometry Mode Decomposition

This paper aims to explore the difficulty of obtaining source signals from complex mixed signals and the issue that the FastICA algorithm cannot directly decompose the received single-channel mixed signals and distort the signal separation in low signal-to-noise environments. Thus, in this work, a comprehensive single-channel mixed signal separation algorithm was proposed based on the combination of Symplectic Geometry Mode Decomposition (SGMD) and the FastICA algorithm. First, SGMD-FastICA uses SGMD to decompose single-channel mixed signals, and then it uses the Pearson correlation coefficient to select the Symplectic Geometry Components that exhibit higher correlation coefficients with the mixed signals. Then, these components are expanded with the single-channel mixed signals into virtual multi-channel signals and input into the FastICA algorithm. The simulation results show that the SGMD algorithm could eliminate noise interference while keeping the raw time series unchanged, which is achievable through symplectic geometry similarity transformation during the decomposition of mixed signals. Comparative experiment results also show that compared with the EMD-FastICA and VMD-FastICA, the SGMD-FastICA algorithm has the best separation effect for single-channel mixed signals. The SGMD-FastICA algorithm represents an improved solution that addresses the limitations of the FastICA algorithm, enabling the direct separation of single-channel mixed signals, while also addressing the challenge of proper signal separation in noisy environments.

Functional neuroimaging biomarkers of anhedonia response to escitalopram plus adjunct aripiprazole treatment for major depressive disorder

BACKGROUND

Identifying neuroimaging biomarkers of antidepressant response may help guide treatment decisions and advance precision medicine.

AIMS

To examine the relationship between anhedonia and functional neurocircuitry in key reward processing brain regions in people with major depressive disorder receiving aripiprazole adjunct therapy with escitalopram.

METHOD

Data were collected as part of the CAN-BIND-1 study. Participants experiencing a current major depressive episode received escitalopram for 8 weeks; escitalopram non-responders received adjunct aripiprazole for an additional 8 weeks. Functional magnetic resonance imaging (on weeks 0 and 8) and clinical assessment of anhedonia (on weeks 0, 8 and 16) were completed. Seed-based correlational analysis was employed to examine the relationship between baseline resting-state functional connectivity (rsFC), using the nucleus accumbens (NAc) and anterior cingulate cortex (ACC) as key regions of interest, and change in anhedonia severity after adjunct aripiprazole.

RESULTS

Anhedonia severity significantly improved after treatment with adjunct aripiprazole.There was a positive correlation between anhedonia improvement and rsFC between the ACC and posterior cingulate cortex, ACC and posterior praecuneus, and NAc and posterior praecuneus. There was a negative correlation between anhedonia improvement and rsFC between the ACC and anterior praecuneus and NAc and anterior praecuneus.

CONCLUSIONS

Eight weeks of aripiprazole, adjunct to escitalopram, was associated with improved anhedonia symptoms. Changes in functional connectivity between key reward regions were associated with anhedonia improvement, suggesting aripiprazole may be an effective treatment for individuals experiencing reward-related deficits. Future studies are required to replicate our findings and explore their generalisability, using other agents with partial dopamine (D2) agonism and/or serotonin (5-HT2A) antagonism.

No changes in triple network engagement following (combined) noradrenergic and glucocorticoid stimulation in healthy men

Successful recovery from stress is integral for adaptive responding to the environment. At a cellular level, this involves (slow genomic) actions of cortisol, which alter or reverse rapid effects of noradrenaline and cortisol associated with acute stress. At the network scale, stress recovery is less well understood but assumed to involve changes within salience-, executive control-, and default mode networks. To date, few studies have investigated this phase and directly tested these assumptions. Here, we present results from a double-blind, placebo-controlled, between-group paradigm (N = 165 healthy males) administering 10 mg oral yohimbine and/or 10 mg oral hydrocortisone two hours prior to resting state scanning. We found no changes in within-network connectivity of the three networks, both after single and combined drug administration. We further report the results of Bayesian parameter inference to provide evidence for the null hypothesis. Our results contrast with previous findings, which may be attributable to systematic differences between paradigms, highlighting the need to isolate paradigm-specific effects from those related to stress.

Should one go for individual- or group-level brain parcellations? A deep-phenotyping benchmark

The analysis and understanding of brain characteristics often require considering region-level information rather than voxel-sampled data. Subject-specific parcellations have been put forward in recent years, as they can adapt to individual brain organization and thus offer more accurate individual summaries than standard atlases. However, the price to pay for adaptability is the lack of group-level consistency of the data representation. Here, we investigate whether the good representations brought by individualized models are merely an effect of circular analysis, in which individual brain features are better represented by subject-specific summaries, or whether this carries over to new individuals, i.e., whether one can actually adapt an existing parcellation to new individuals and still obtain good summaries in these individuals. For this, we adapt a dictionary-learning method to produce brain parcellations. We use it on a deep-phenotyping dataset to assess quantitatively the patterns of activity obtained under naturalistic and controlled-task-based settings. We show that the benefits of individual parcellations are substantial, but that they vary a lot across brain systems.

Functional neural networks stratify Parkinson's disease patients across the spectrum of cognitive impairment

INTRODUCTION

Cognitive impairment (CI) is a significant non-motor symptoms in Parkinson's disease (PD) that often precedes the emergence of motor symptoms by several years. Patients with PD hypothetically progress from stages without CI (PD-normal cognition [NC]) to stages with Mild CI (PD-MCI) and PD dementia (PDD). CI symptoms in PD are linked to different brain regions and neural pathways, in addition to being the result of dysfunctional subcortical regions. However, it is still unknown how functional dysregulation correlates to progression during the CI. Neuroimaging techniques hold promise in discriminating CI stages of PD and further contribute to the biomarker formation of CI in PD. In this study, we explore disparities in the clinical assessments and resting-state functional connectivity (FC) among three CI stages of PD.

METHODS

We enrolled 88 patients with PD and 26 healthy controls (HC) for a cross sectional clinical study and performed intra- and inter-network FC analysis in conjunction with comprehensive clinical cognitive assessment.

RESULTS

Our findings underscore the significance of several neural networks, namely, the default mode network (DMN), frontoparietal network (FPN), dorsal attention network, and visual network (VN) and their inter-intra-network FC in differentiating between PD-MCI and PDD. Additionally, our results showed the importance of sensory motor network, VN, DMN, and salience network (SN) in the discriminating PD-NC from PDD. Finally, in comparison to HC, we found DMN, FPN, VN, and SN as pivotal networks for further differential diagnosis of CI stages of PD.

CONCLUSION

We propose that resting-state networks (RSN) can be a discriminating factor in distinguishing the CI stages of PD and progressing from PD-NC to MCI or PDD. The integration of clinical and neuroimaging data may enhance the early detection of PD in clinical settings and potentially prevent the disease from advancing to more severe stages.

Type 2 Diabetes and Biomarkers of Brain Structure, Perfusion, Metabolism, and Function in Late Mid-Life: A Multimodal Discordant Twin Study

BACKGROUND

Type 2 diabetes (T2D) is associated with an increased risk of dementia and early features may become evident even in mid-life. Characterizing these early features comprehensively requires multiple measurement modalities and careful selection of participants with and without T2D.

OBJECTIVE

We conducted a cross-sectional multimodal imaging study of T2D-discordant twins in late mid-life to provide insights into underlying mechanisms.

METHODS

Measurements included computerized cognitive battery, brain MRI (including arterial spin labelling, diffusion tensor, resting state functional), fluorodeoxyglucose (FDG)-PET, and retinal optical coherence tomography.

RESULTS

There were 23 pairs, mean age 63.7 (±6.1) years. In global analyses, T2D was associated with poorer attention (β= -0.45, p <0.001) and with reduced FDG uptake (β= -5.04, p = 0.02), but not with cortical thickness (p = 0.71), total brain volume (p = 0.51), fractional anisotropy (p = 0.15), mean diffusivity (p = 0.34), or resting state activity (p = 0.4). Higher FDG uptake was associated with better attention (β= 3.19, p = 0.01) but not with other cognitive domains. In regional analyses, T2D was associated with lower accumbens volume (β= -44, p = 0.0004) which was in turn associated with poorer attention.

CONCLUSION

T2D-related brain dysfunction in mid-life manifests as attentional loss accompanied by evidence of subtle neurodegeneration and global reduction in cerebral metabolism, in the absence of overt cerebrovascular disease.

Investigating cognitive reserve, symptom resolution and brain connectivity in mild traumatic brain injury

BACKGROUND

A proportion of patients with mild traumatic brain injury (mTBI) suffer long-term consequences, and the reasons behind this are still poorly understood. One factor that may affect outcomes is cognitive reserve, which is the brain's ability to maintain cognitive function despite injury. It is often assessed through educational level or premorbid IQ tests. This study aimed to explore whether there were differences in post-concussion symptoms and symptom resolution between patients with mTBI and minor orthopedic injuries one week and three months after injury. Additional aims were to explore the relationship between cognitive reserve and outcome, as well as functional connectivity according to resting state functional magnetic resonance imaging (rs-fMRI).

METHOD

Fifteen patients with mTBI and 15 controls with minor orthopedic injuries were recruited from the emergency department. Assessments, including Rivermead Post-Concussion Questionnaire (RPQ), neuropsychological testing, and rs-fMRI scans, were conducted on average 7 days (SD = 2) and 122 days (SD = 51) after injury.

RESULTS

At the first time point, significantly higher rates of post-concussion symptoms (U = 40.0, p = 0.003), state fatigue (U = 56.5, p = 0.014), and fatigability (U = 58.5, p = 0.025) were observed among the mTBI group than among the controls. However, after three months, only the difference in post-concussion symptoms remained significant (U = 27.0, p = 0.003). Improvement in post-concussion symptoms was found to be significantly correlated with cognitive reserve, but only in the mTBI group (Spearman's rho = -0.579, p = .038). Differences in the trajectory of recovery were also observed for fatigability between the two groups (U = 36.5, p = 0.015). Moreover, functional connectivity differences in the frontoparietal network were observed between the groups, and for mTBI patients, functional connectivity differences in an executive control network were observed over time.

CONCLUSION

The findings of this pilot study suggest that mTBI, compared to minor orthopedic trauma, is associated to both functional connectivity changes in the brain and concussion-related symptoms. While there is improvement in these symptoms over time, a small subgroup with lower cognitive reserve appears to experience more persistent and possibly worsening symptoms over time. This, however, needs to be validated in larger studies.

TRIAL REGISTRATION

NCT05593172. Retrospectively registered.

Functional connectivity patterns in parosmia

OBJECTIVE

Parosmia is a qualitative olfactory dysfunction presenting as "distorted odor perception" in presence of an odor source. Aim of this study was to use resting state functional connectivity to gain more information on the alteration of olfactory processing at the level of the central nervous system level.

METHODS

A cross sectional study was performed in 145 patients with parosmia (age range 20-76 years; 90 women). Presence and degree of parosmia was diagnosed on the basis of standardized questionnaires. Participants also received olfactory testing using the "Sniffin' Sticks". Then they underwent resting state scans using a 3 T magnetic resonance imaging scanner while fixating on a cross.

RESULTS

Whole brain analyses revealed reduced functional connectivity in salience as well as executive control networks. Region of interest-based analyses also supported reduced functional connectivity measures between primary and secondary olfactory eloquent areas (temporal pole, supramarginal gyrus and right orbitofrontal cortex; dorso-lateral pre-frontal cortex and the right piriform cortex).

CONCLUSIONS

Participants with parosmia exhibited a reduced information flow between memory, decision making centers, and primary and secondary olfactory areas.

Differences in Olivo-Cerebellar Circuit and Cerebellar Network Connectivity in Essential Tremor: a Resting State fMRI Study

The olivo-cerebellar circuit is thought to play a crucial role in the pathophysiology of essential tremor (ET). Whether olivo-cerebellar circuit dysfunction is also present at rest, in the absence of clinical tremor and linked voluntary movement, remains unclear. Assessing this network in detail with fMRI is challenging, considering the brainstem is close to major arteries and pulsatile cerebrospinal fluid-filled spaces obscuring signals of interest. Here, we used methods tailored to the analysis of infratentorial structures. We hypothesize that the olivo-cerebellar circuit shows altered intra-network connectivity at rest and decreased functional coupling with other parts of the motor network in ET. In 17 ET patients and 19 healthy controls, we investigated using resting state fMRI intracerebellar functional and effective connectivity on a dedicated cerebellar atlas. With independent component analysis, we investigated data-driven cerebellar motor network activations during rest. Finally, whole-brain connectivity of cerebellar motor structures was investigated using identified components. In ET, olivo-cerebellar pathways show decreased functional connectivity compared with healthy controls. Effective connectivity analysis showed an increased inhibitory influence of the dentate nucleus towards the inferior olive. Cerebellar independent component analyses showed motor resting state networks are less strongly connected to the cerebral cortex compared to controls. Our results indicate the olivo-cerebellar circuit to be affected at rest. Also, the cerebellum is "disconnected" from the rest of the motor network. Aberrant activity, generated within the olivo-cerebellar circuit could, during action, spread towards other parts of the motor circuit and potentially underlie the characteristic tremor of this patient group.

Altered Large-Scale Resting-State Functional Network Connectivity in Convergence Insufficiency Young Adults Compared With Binocularly Normal Controls

Purpose

To investigate the underlying resting-state functional connectivity (RSFC) of symptomatic convergence insufficiency (CI) compared with binocularly normal controls (BNC) using functional magnetic resonance imaging (fMRI) under The Convergence Insufficiency Neuro‑mechanism Adult Population Study (NCT03593031).

Methods

A total of 101 participants were eligible for this study. After removing datasets with motion artifacts, 49 CI and 47 BNC resting-state functional magnetic resonance imaging datasets were analyzed. CI was diagnosed with the following signs: (1) receded near point of convergence of 6 cm or greater, (2) decreased positive fusional vergence of less than 15∆ or failing Sheard's criteria of twice the near phoria, (3) near phoria of at least 4∆ more exophoric compared with the distance phoria, and (4) symptoms using the Convergence Insufficiency Symptom Survey (score of ≥21). RSFC was assessed using a group-level independent components analysis and dual regression. A behavioral correlation analysis using linear regression method was performed between clinical measures and RSFC using the significant difference between the CI and BNC.

Results

On average, a decreased RSFC was observed within the frontoparietal network, default mode network and visual network in patients with CI, compared with the participants with BNC (P < 0.05, corrected for multiple comparisons). The default mode network RSFC strength was significantly correlated with the PFV, near point of convergence, and difference between the horizontal phoria at near compared with far (P < 0.05).

Conclusions

Results support altered RSFC in patients with CI compared with participants with BNC and suggest that these differences in underlying neurophysiology may in part be in connection with the differences in optometric visual function used to diagnose CI.

Protocol for a systematic review and meta-analysis of coordinate-based network mapping of brain structure in bipolar disorder across the lifespan

BACKGROUND

Studies about brain structure in bipolar disorder have reported conflicting findings. These findings may be explained by the high degree of heterogeneity within bipolar disorder, especially if structural differences are mapped to single brain regions rather than networks.

AIMS

We aim to complete a systematic review and meta-analysis to identify brain networks underlying structural abnormalities observed on T1-weighted magnetic resonance imaging scans in bipolar disorder across the lifespan. We also aim to explore how these brain networks are affected by sociodemographic and clinical heterogeneity in bipolar disorder.

METHOD

We will include case-control studies that focus on whole-brain analyses of structural differences between participants of any age with a standardised diagnosis of bipolar disorder and controls. The electronic databases Medline, PsycINFO and Web of Science will be searched. We will complete an activation likelihood estimation analysis and a novel coordinate-based network mapping approach to identify specific brain regions and brain circuits affected in bipolar disorder or relevant subgroups. Meta-regressions will examine the effect of sociodemographic and clinical variables on identified brain circuits.

CONCLUSIONS

Findings from this systematic review and meta-analysis will enhance understanding of the pathophysiology of bipolar disorder. The results will identify brain circuitry implicated in bipolar disorder, and how they may relate to relevant sociodemographic and clinical variables across the lifespan.

A systematic review and meta-analysis of resting-state fMRI in anxiety disorders: Need for data sharing to move the field forward

Anxiety disorders are among the most prevalent psychiatric disorders. Neuroimaging findings remain uncertain, and resting state functional magnetic resonance (rs-fMRI) connectivity is of particular interest since it is a scalable functional imaging modality. Given heterogeneous past findings for rs-fMRI in anxious individuals, we characterize patterns across anxiety disorders by conducting a systematic review and meta-analysis. Studies were included if they contained at the time of scanning both a healthy group and a patient group. Due to insufficient study numbers, the quantitative meta-analysis only included seed-based studies. We performed an activation likelihood estimation (ALE) analysis that compared patients and healthy volunteers. All analyses were corrected for family-wise error with a cluster-level threshold of p < .05. Patients exhibited hypo-connectivity between the amygdala and the medial frontal gyrus, anterior cingulate cortex, and cingulate gyrus. This finding, however, was not robust to potential file-drawer effects. Though limited by strict inclusion criteria, our results highlight the heterogeneous nature of reported findings. This underscores the need for data sharing when attempting to detect reliable patterns of disruption in brain activity across anxiety disorders.

Controversies and progress on standardization of large-scale brain network nomenclature

Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.

Individual differences in time-varying and stationary brain connectivity during movie watching from childhood to early adulthood: Age, sex, and behavioral associations

Spatially remote brain regions exhibit dynamic functional interactions across various task conditions. While time-varying functional connectivity during movie watching shows sensitivity to movie content, stationary functional connectivity remains relatively stable across videos. These findings suggest that dynamic and stationary functional interactions may represent different aspects of brain function. However, the relationship between individual differences in time-varying and stationary connectivity and behavioral phenotypes remains elusive. To address this gap, we analyzed an open-access functional MRI dataset comprising participants aged 5-22 years, who watched two cartoon movie clips. We calculated regional brain activity, time-varying connectivity, and stationary connectivity, examining associations with age, sex, and behavioral assessments. Model comparison revealed that time-varying connectivity was more sensitive to age and sex effects compared with stationary connectivity. The preferred age models exhibited quadratic log age or quadratic age effects, indicative of inverted-U shaped developmental patterns. In addition, females showed higher consistency in regional brain activity and time-varying connectivity than males. However, in terms of behavioral predictions, only stationary connectivity demonstrated the ability to predict full-scale intelligence quotient. These findings suggest that individual differences in time-varying and stationary connectivity may capture distinct aspects of behavioral phenotypes.

Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi-site MRI data

Combining magnetic resonance imaging (MRI) data from multi-site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the scanner/site variability is a significant confound that complicates the interpretation of the results, so effective and complete removal of the scanner/site variability is necessary to realise the full advantages of pooling multi-site datasets. Independent component analysis (ICA) and general linear model (GLM) based harmonisation methods are the two primary methods used to eliminate scanner/site effects. Unfortunately, there are challenges with both ICA-based and GLM-based harmonisation methods to remove site effects completely when the signals of interest and scanner/site effects-related variables are correlated, which may occur in neuroscience studies. In this study, we propose an effective and powerful harmonisation strategy that implements dual projection (DP) theory based on ICA to remove the scanner/site effects more completely. This method can separate the signal effects correlated with site variables from the identified site effects for removal without losing signals of interest. Both simulations and vivo structural MRI datasets, including a dataset from Autism Brain Imaging Data Exchange II and a travelling subject dataset from the Strategic Research Program for Brain Sciences, were used to test the performance of a DP-based ICA harmonisation method. Results show that DP-based ICA harmonisation has superior performance for removing site effects and enhancing the sensitivity to detect signals of interest as compared with GLM-based and conventional ICA harmonisation methods.

Improved processing speed and decreased functional connectivity in individuals with chronic stroke after paired exercise and motor training

After stroke, impaired motor performance is linked to an increased demand for cognitive resources. Aerobic exercise improves cognitive function in neurologically intact populations and may be effective in altering cognitive function post-stroke. We sought to determine if high-intensity aerobic exercise paired with motor training in individuals with chronic stroke alters cognitive-motor function and functional connectivity between the dorsolateral prefrontal cortex (DLPFC), a key region for cognitive-motor processes, and the sensorimotor network. Twenty-five participants with chronic stroke were randomly assigned to exercise (n = 14; 66 ± 11 years; 4 females), or control (n = 11; 68 ± 8 years; 2 females) groups. Both groups performed 5-days of paretic upper limb motor training after either high-intensity aerobic exercise (3 intervals of 3 min each, total exercise duration of 23-min) or watching a documentary (control). Resting-state fMRI, and trail making test part A (TMT-A) and B were recorded pre- and post-intervention. Both groups showed implicit motor sequence learning (p < 0.001); there was no added benefit of exercise for implicit motor sequence learning (p = 0.738). The exercise group experienced greater overall cognitive-motor improvements measured with the TMT-A. Regardless of group, the changes in task score, and dwell time during TMT-A were correlated with a decrease in DLPFC-sensorimotor network functional connectivity (task score: p = 0.025; dwell time: p = 0.043), which is thought to reflect a reduction in the cognitive demand and increased automaticity. Aerobic exercise may improve cognitive-motor processing speed post-stroke.

Influence of natalizumab on resting-state connectivity in patients with multiple sclerosis

Background

Changes in brain connectivity occur in patients with multiple sclerosis (MS), even in patients under disease-modifying therapies. Using magnetic resonance imaging (MRI) to asses patients treated with disease-modifying therapies, such as natalizumab, can elucidate the mechanisms involved in clinical deterioration in MS.

Objectives

To evaluate differences in resting-state functional connectivity among MS patients treated with natalizumab, MS patients not treated with natalizumab, and controls.

Design

Single-center retrospective cross-sectional study.

Methods

Twenty-three MS patients being treated with natalizumab were retrospectively compared with 23 MS patients who were naïve for natalizumab, and were using first-line medications (interferon-β and/or glatiramer acetate), and 17 gender- and age-matched control subjects. The MS patient groups were also matched for time since diagnosis and hyperintense lesion volume on FLAIR. All participants underwent brain MRI using a 3 Tesla scanner. Independent component analysis and dual regression were used to identify resting-state functional connectivity using the FMRIB Software Library.

Results

In comparison to controls, the MS patients treated with natalizumab presented decreased connectivity in the left orbitofrontal cortex, in the anterior cingulate and orbitofrontal cortex network. The patients not treated with natalizumab presented increased connectivity in the secondary visual, sensorimotor, and ventral attention networks in comparison to controls.Compared to patients treated with natalizumab, the patients not using natalizumab presented increased connectivity in the left Heschl's gyrus and in the right superior frontal gyrus in the ventral attention network.

Conclusion

Differences in brain connectivity between MS patients not treated with natalizumab, healthy controls, and patients treated with natalizumab may be secondary to suboptimal neuronal compensation due to prior less efficient treatments, or due to a compensation in response to maladaptive plasticity.