Exploring mechanisms of anhedonia in depression through neuroimaging and data-driven approaches.

BACKGROUND: Anhedonia is a core symptom of depression that is closely related to prognosis and treatment outcomes. However, accurate and efficient treatments for anhedonia are lacking, mandating a deeper understanding of the underlying mechanisms. METHODS: A total of 303 patients diagnosed with depression and anhedonia were assessed by the Snaith-Hamilton Pleasure Scale (SHAPS) and magnetic resonance imaging (MRI). The patients were categorized into a low-anhedonia group and a high-anhedonia group using the K-means algorithm. A data-driven approach was used to explore the differences in brain structure and function with different degrees of anhedonia based on MATLAB. A random forest model was used exploratorily to test the predictive ability of differences in brain structure and function on anhedonia in depression. RESULTS: Structural and functional differences were apparent in several brain regions of patients with depression and high-level anhedonia, including in the temporal lobe, paracingulate gyrus, superior frontal gyrus, inferior occipital gyrus, right insular gyrus, and superior parietal lobule. And changes in these brain regions were significantly correlated with scores of SHAPS. CONCLUSIONS: These brain regions may be useful as biomarkers that provide a more objective assessment of anhedonia in depression, laying the foundation for precision medicine in this treatment-resistant, relatively poor prognosis group.

Convolutional neural networks can detect orthostatic hypotension in Parkinson's disease using resting-state functional near-infrared spectroscopy data.

Neurological disorders such as Parkinson's disease (PD) often adversely affect the vascular system, leading to alterations in blood flow patterns. Functional near-infrared spectroscopy (fNIRS) is used to monitor hemodynamic changes via signal measurement. This study investigated the potential of using resting-state fNIRS data through a convolutional neural network (CNN) to evaluate PD with orthostatic hypotension. The CNN demonstrated significant efficacy in analyzing fNIRS data, and it outperformed the other machine learning methods. The results indicate that judicious input data selection can enhance accuracy by over 85%, while including the correlation matrix as an input further improves the accuracy to more than 90%. This study underscores the promising role of CNN-based fNIRS data analysis in the diagnosis and management of the PD. This approach enhances diagnostic accuracy, particularly in resting-state conditions, and can reduce the discomfort and risks associated with current diagnostic methods, such as the head-up tilt test.

Alterations of resting-state network dynamics in Alzheimer's disease based on leading eigenvector dynamics analysis.

Alzheimer's disease (AD) is a neurodegenerative disease, and mild cognitive impairment (MCI) is considered a transitional stage between healthy aging and dementia. Early detection of MCI can help slow down the progression of AD. At present, there are few studies exploring the characteristics of abnormal dynamic brain activity in AD. This article uses a method called Leading Eigenvector Dynamics Analysis (LEiDA) to study resting-state functional magnetic resonance imaging (rs-fMRI) data of AD, MCI, and cognitively normal (CN) participants. By identifying repetitive states of phase coherence, inter group differences in brain dynamic activity indicators are examined. And the neurobehavioral scales were used to assess the relationship between abnormal dynamic activities and cognitive function. The results showed that in the indicators of occurrence probability and lifetime, the globally synchronized state of the patient group decreased. The activity state of the limbic regions significantly detected the difference between AD and the other two groups. Compared to CN, AD and MCI have varying degrees of increase in default and visual regions activity states. In addition, in the analysis related to the cognitive scales, it was found that individuals with poorer cognitive abilities were less active in the globally synchronized state, and more active in limbic regions activity state and visual regions activity state. Taken together, these findings reveal abnormal dynamic activity of resting-state networks in patients with AD and MCI, provide new insights into the dynamic analysis of brain networks, and contribute to a deeper understanding of abnormal spatial dynamic patterns in AD patients.

Utilizing connectome fingerprinting functional MRI models for motor activity prediction in presurgical planning: A feasibility study.

Presurgical planning prior to brain tumor resection is critical for the preservation of neurologic function post-operatively. Neurosurgeons increasingly use advanced brain mapping techniques pre- and intra-operatively to delineate brain regions which are "eloquent" and should be spared during resection. Functional MRI (fMRI) has emerged as a commonly used non-invasive modality for individual patient mapping of critical cortical regions such as motor, language, and visual cortices. To map motor function, patients are scanned using fMRI while they perform various motor tasks to identify brain networks critical for motor performance, but it may be difficult for some patients to perform tasks in the scanner due to pre-existing deficits. Connectome fingerprinting (CF) is a machine-learning approach that learns associations between resting-state functional networks of a brain region and the activations in the region for specific tasks; once a CF model is constructed, individualized predictions of task activation can be generated from resting-state data. Here we utilized CF to train models on high-quality data from 208 subjects in the Human Connectome Project (HCP) and used this to predict task activations in our cohort of healthy control subjects (n = 15) and presurgical patients (n = 16) using resting-state fMRI (rs-fMRI) data. The prediction quality was validated with task fMRI data in the healthy controls and patients. We found that the task predictions for motor areas are on par with actual task activations in most healthy subjects (model accuracy around 90%-100% of task stability) and some patients suggesting the CF models can be reliably substituted where task data is either not possible to collect or hard for subjects to perform. We were also able to make robust predictions in cases in which there were no task-related activations elicited. The findings demonstrate the utility of the CF approach for predicting activations in out-of-sample subjects, across sites and scanners, and in patient populations. This work supports the feasibility of the application of CF models to presurgical planning, while also revealing challenges to be addressed in future developments. PRACTITIONER POINTS: Precision motor network prediction using connectome fingerprinting. Carefully trained models' performance limited by stability of task-fMRI data. Successful cross-scanner predictions and motor network mapping in patients with tumor.

Prenatal and childhood air pollution exposure, cellular immune biomarkers, and brain connectivity in early adolescents.

Introduction: Ambient air pollution is a neurotoxicant with hypothesized immune-related mechanisms. Adolescent brain structural and functional connectivity may be especially vulnerable to ambient pollution due to the refinement of large-scale brain networks during this period, which vary by sex and have important implications for cognitive, behavioral, and emotional functioning. In the current study we explored associations between air pollutants, immune markers, and structural and functional connectivity in early adolescence by leveraging cross-sectional sex-stratified data from the Adolescent Brain Cognitive Development℠ Study®. Methods: Pollutant concentrations of fine particulate matter, nitrogen dioxide, and ozone were assigned to each child's primary residential address during the prenatal period and childhood (9-10 years-old) using an ensemble-based modeling approach. Data collected at 11-13 years-old included resting-state functional connectivity of the default mode, frontoparietal, and salience networks and limbic regions of interest, intracellular directional and isotropic diffusion of available white matter tracts, and markers of cellular immune activation. Using partial least squares correlation, a multivariate data-driven method that identifies important variables within latent dimensions, we investigated associations between 1) pollutants and structural and functional connectivity, 2) pollutants and immune markers, and 3) immune markers and structural and functional connectivity, in each sex separately. Results: Air pollution exposure was related to white matter intracellular directional and isotropic diffusion at ages 11-13 years, but the direction of associations varied by sex. There were no associations between pollutants and resting-state functional connectivity at ages 11-13 years. Childhood exposure to nitrogen dioxide was negatively correlated with white blood cell count in males. Immune biomarkers were positively correlated with white matter intracellular directional diffusion in females and both white matter intracellular directional and isotropic diffusion in males. Lastly, there was a reliable negative correlation between lymphocyte-to-monocyte ratio and default mode network resting-state functional connectivity in females, as well as a compromised immune marker profile associated with lower resting-state functional connectivity between the salience network and the left hippocampus in males. In post-hoc exploratory analyses, we found that the PLSC-identified white matter tracts and resting-state networks related to processing speed and cognitive control performance from the NIH Toolbox. Conclusions: We identified novel links between childhood nitrogen dioxide and cellular immune activation in males, and brain network connectivity and immune markers in both sexes. Future research should explore the potentially mediating role of immune activity in how pollutants affect neurological outcomes as well as the potential consequences of immune-related patterns of brain connectivity in service of improved brain health for all.

Aberrant baseline brain activity and disrupted functional connectivity in patients with vascular cognitive impairment due to cerebral small vessel disease.

Objective: This study aims to examine the alterations in aberrant brain activity and network connectivity between individuals with mild and major vascular cognitive impairment (VCI). Materials and methods: A total of 114 patients with cerebral small vessel disease (CSVD) were included in this study, comprising 61 individuals with mild VCI (mean age, 55.7 ± 6.9 years; male, 42.6%) and 53 cases with major VCI (mean age, 57.6 ± 5.5 years; male, 58.5%). Additionally, 53 age-, gender-, and education-matched healthy subjects were recruited as normal controls (NC) (mean age, 54.9 ± 7.9 years; male, 52.9%). All participants underwent neuropsychological assessments and magnetic resonance imaging scans. One-way analysis of variance was used to compare fractional amplitude of low-frequency fluctuation (fALFF) values among the three groups. Two-sample t-tests were conducted to assess functional connectivity matrices between different groups for each connection. Moreover, mediation analyses were performed to explore the mediating effect of aberrant brain activity on the relationship between cognitive impairment and CSVD total burden. Results: VCI patients exhibited aberrant brain activity in regions such as the right thalamus (THA_R), right cuneus (CUN_R), left postcentral gyrus (PoCG_L), right postcentral gyrus (PoCG_R), right median cingulate, paracingulate gyri (PCG_R), and left precuneus (PCUN_L). Reduced positive functional connectivity was predominantly observed among nodes including PCUN_L, CUN_R, PoCG_L, PoCG_R, right posterior cingulate (PCG_R), and left occipital gyrus (IOG_L) in VCI patients. The aberrant baseline brain activity and disrupted brain network were more pronounced with worsening cognitive function. Increased fALFF values in THA_R, CUN_R, and PoCG_L mediated cognitive impairment in CSVD patients. Conclusion: Abnormal brain activities in THA_R, CUN_R, and PoCG_L, along with their associated abnormal functional connections, play a significant role in VCI. The study revealed a progressive increase in aberrant brain activity and network connectivity with advancing stages of VCI.

Comparisons of the amplitude of low-frequency fluctuation and functional connectivity in major depressive disorder and social anxiety disorder: A resting-state fMRI study.

BACKGROUND: Studies comparing the brain functions of major depressive disorder (MDD) and social anxiety disorder (SAD) at the regional and network levels remain scarce. This study aimed to elucidate their pathogenesis using neuroimaging techniques and explore biomarkers that can differentiate these disorders. METHODS: Resting-state fMRI data were collected from 48 patients with MDD, 41 patients with SAD, and 82 healthy controls. Differences in the amplitude of low-frequency fluctuations (ALFF) among the three groups were examined to identify regions showing abnormal regional spontaneous activity. A seed-based functional connectivity (FC) analysis was conducted using ALFF results as seeds and different connections were identified between regions showing abnormal local spontaneous activity and other regions. The correlation between abnormal brain function and clinical symptoms was analyzed. RESULTS: Patients with MDD and SAD exhibited similar abnormal ALFF and FC in several brain regions; notably, FC between the right superior frontal gyrus (SFG) and the right posterior supramarginal gyrus (pSMG) in patients with SAD was negatively correlated with depressive symptoms. Furthermore, patients with MDD showed higher ALFF in the right SFG than HCs and those with SAD. LIMITATION: Potential effects of medications, comorbidities, and data type could not be ignored. CONCLUSION: MDD and SAD showed common and distinct aberrant brain function patterns at the regional and network levels. At the regional level, we found that the ALFF in the right SFG was different between patients with MDD and those with SAD. At the network level, we did not find any differences between these disorders.

Measuring functional connectivity in frequency-domain helps to better characterize brain function.

Resting-state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence-based and correlation-based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging.

Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy.

Whole-brain functional connectivity networks (connectomes) have been characterized at different scales in humans using EEG and fMRI. Multimodal epileptic networks have also been investigated, but the relationship between EEG and fMRI defined networks on a whole-brain scale is unclear. A unified multimodal connectome description, mapping healthy and pathological networks would close this knowledge gap. Here, we characterize the spatial correlation between the EEG and fMRI connectomes in right and left temporal lobe epilepsy (rTLE/lTLE). From two centers, we acquired resting-state concurrent EEG-fMRI of 35 healthy controls and 34 TLE patients. EEG-fMRI data was projected into the Desikan brain atlas, and functional connectomes from both modalities were correlated. EEG and fMRI connectomes were moderately correlated. This correlation was increased in rTLE when compared to controls for EEG-delta/theta/alpha/beta. Conversely, multimodal correlation in lTLE was decreased in respect to controls for EEG-beta. While the alteration was global in rTLE, in lTLE it was locally linked to the default mode network. The increased multimodal correlation in rTLE and decreased correlation in lTLE suggests a modality-specific lateralized differential reorganization in TLE, which needs to be considered when comparing results from different modalities. Each modality provides distinct information, highlighting the benefit of multimodal assessment in epilepsy.

The relationship between resting-state hemodynamic (fMRI) and electrophysiological (EEG) connectivity has been investigated in healthy subjects, but this relationship is unknown in patients with left and right temporal lobe epilepsies (l/rTLE). Does the magnitude of the relationship differ between healthy subjects and patients? What role does the laterality of the epileptic focus play? What are the spatial contributions to this relationship? Here we use concurrent EEG-fMRI recordings of 65 subjects from two centers (35 controls, 34 TLE patients), to assess the correlation between EEG and fMRI connectivity. For all datasets, frequency-specific changes in cross-modal correlation were seen in lTLE and rTLE. EEG and fMRI connectivities do not measure perfectly overlapping brain networks and provide distinct information on brain networks altered in TLE, highlighting the benefit of multimodal assessment to inform about normal and pathological brain function.

Dragon boat exercise reshapes the temporal-spatial dynamics of the brain.

Although exercise training has been shown to enhance neurological function, there is a shortage of research on how exercise training affects the temporal-spatial synchronization properties of functional networks, which are crucial to the neurological system. This study recruited 23 professional and 24 amateur dragon boat racers to perform simulated paddling on ergometers while recording EEG. The spatiotemporal dynamics of the brain were analyzed using microstates and omega complexity. Temporal dynamics results showed that microstate D, which is associated with attentional networks, appeared significantly altered, with significantly higher duration, occurrence, and coverage in the professional group than in the amateur group. The transition probabilities of microstate D exhibited a similar pattern. The spatial dynamics results showed the professional group had lower brain complexity than the amateur group, with a significant decrease in omega complexity in the α (8-12 Hz) and ß (13-30 Hz) bands. Dragon boat training may strengthen the attentive network and reduce the complexity of the brain. This study provides evidence that dragon boat exercise improves the efficiency of the cerebral functional networks on a spatiotemporal scale.

Investigating the relationship between resting-state EEG microstates and executive functions: A null finding.

Recent advances in cognitive neurosciences suggest that intrinsic brain networks dynamics are associated with cognitive functioning. Despite this emerging perspective, limited research exists to validate this hypothesis. This Registered Report aimed to specifically test the relationship between intrinsic brain spatio-temporal dynamics and executive functions. Resting-state EEG microstates were used to assess brain spatio-temporal dynamics, while a comprehensive battery of nine cognitive function tasks was employed to evaluate executive functions in 140 participants. We hypothesized that microstates (class C and D) metrics would correlate with an executive functions composite score. Contrary to expectations, our hypotheses were not supported by the data. We however observed a small, non-significant trend with a negative correlation between microstate D occurrences and executive functions scores (r = -.18, 95% CI [-.33, -.01]) which however did not meet the adjusted threshold for significance. In light of the inconclusive or minor effect sizes observed, the assertion that intrinsic brain networks dynamics - as measured by resting-state EEG microstate metrics - are a reliable signature of executive functioning remains unsupported.

Spontaneous cortical activity is altered in persons with HIV and related to domain-specific cognitive function.

Whilst the average lifespan of persons with HIV now approximates that of the general population, these individuals are at a much higher risk of developing cognitive impairment with ∼35-70% experiencing at least subtle cognitive deficits. Previous works suggest that HIV impacts both low-level primary sensory regions and higher-level association cortices. Notably, multiple neuroHIV studies have reported elevated levels of spontaneous cortical activity during the pre-stimulus baseline period of task-based experiments, but only a few have examined such activity during resting-state conditions. In the current study, we examined such spontaneous cortical activity using magnetoencephalography in 79 persons with HIV and 83 demographically matched seronegative controls and related this neural activity to performance on neuropsychological assessments of cognitive function. Consistent with previous works, persons with HIV exhibited stronger spontaneous gamma activity, particularly in inferior parietal, prefrontal and superior temporal cortices. In addition, serostatus moderated the relationship between spontaneous beta activity and attention, motor and processing speed scores, with controls but not persons with HIV showing stronger beta activity with better performance. The current results suggest that HIV predominantly impacts spontaneous activity in association cortices, consistent with alterations in higher-order brain function, and may be attributable to deficient GABAergic signalling, given its known role in the generation of gamma and beta oscillations. Overall, these effects align with previous studies showing aberrant spontaneous activity in persons with HIV and provide a critical new linkage to domain-specific cognitive dysfunction.

Connectome predictive modeling of trait mindfulness.

Introduction: Trait mindfulness refers to one's disposition or tendency to pay attention to their experiences in the present moment, in a non-judgmental and accepting way. Trait mindfulness has been robustly associated with positive mental health outcomes, but its neural underpinnings are poorly understood. Prior resting-state fMRI studies have associated trait mindfulness with within- and between-network connectivity of the default-mode (DMN), fronto-parietal (FPN), and salience networks. However, it is unclear how generalizable the findings are, how they relate to different components of trait mindfulness, and how other networks and brain areas may be involved. Methods: To address these gaps, we conducted the largest resting-state fMRI study of trait mindfulness to-date, consisting of a pre-registered connectome predictive modeling analysis in 367 adults across three samples collected at different sites. Results: In the model-training dataset, we did not find connections that predicted overall trait mindfulness, but we identified neural models of two mindfulness subscales, Acting with Awareness and Non-judging. Models included both positive networks (sets of pairwise connections that positively predicted mindfulness with increasing connectivity) and negative networks, which showed the inverse relationship. The Acting with Awareness and Non-judging positive network models showed distinct network representations involving FPN and DMN, respectively. The negative network models, which overlapped significantly across subscales, involved connections across the whole brain with prominent involvement of somatomotor, visual and DMN networks. Only the negative networks generalized to predict subscale scores out-of-sample, and not across both test datasets. Predictions from both models were also negatively correlated with predictions from a well-established mind-wandering connectome model. Conclusions: We present preliminary neural evidence for a generalizable connectivity models of trait mindfulness based on specific affective and cognitive facets. However, the incomplete generalization of the models across all sites and scanners, limited stability of the models, as well as the substantial overlap between the models, underscores the difficulty of finding robust brain markers of mindfulness facets.

Connectivity study on resting-state EEG between motor imagery BCI-literate and BCI-illiterate groups.

OBJECTIVE: Although Motor Imagery-based Brain-Computer Interface (MI-BCI) holds significant potential, its practical application faces challenges such as BCI-illiteracy. To mitigate this issue, researchers have attempted to predict BCI-illiteracy by using the resting state, as this was found to be associated with BCI performance. As connectivity's significance in neuroscience has grown, BCI researchers have applied connectivity to it. However, the issues of connectivity have not been considered fully. First, although various connectivity metrics exist, only some have been used to predict BCI-illiteracy. This is problematic because each metric has a distinct hypothesis and perspective to estimate connectivity, resulting in different outcomes according to the metric. Second, the frequency range affects the connectivity estimation. In addition, it is still unknown whether each metric has its own optimal frequency range. Third, the way that estimating connectivity may vary depending upon the dataset has not been investigated. Meanwhile, we still do not know a great deal about how the resting state EEG network differs between BCI-literacy and -illiteracy. APPROACH: To address the issues above, we analysed three large public EEG datasets using three functional connectivity (FC) and three effective connectivity (EC) metrics by employing diverse graph theory measures. Our analysis revealed that the appropriate frequency range to predict BCI-illiteracy varies depending upon the metric. The alpha range was found to be suitable for the metrics of the frequency domain, while alpha + theta were found to be appropriate for Multivariate Granger Causality (MVGC). The difference in network efficiency between BCI-literate and -illiterate groups was constant regardless of the metrics and datasets used. Although we observed that BCI-literacy had stronger connectivity, no other significant constructional differences were found. SIGNIFICANCE: Based upon our findings, we predicted MI-BCI performance for the entire dataset. We discovered that combining several graph features could improve the prediction's accuracy.

Loneliness and Resting-State Functional Brain Connectivity Among Older Adults: A Proportional Correlation.

OBJECTIVE: Loneliness reportedly increases the risk of dementia, especially Alzheimer's disease (AD). The authors' previous study demonstrated associations between loneliness and structural abnormalities observed in early-stage AD. The present study examined associations between the brain's functional characteristics and loneliness among older adults with concerns about cognitive decline. METHODS: This single-center study included 43 participants (13 with amnestic mild cognitive impairment and 30 with normal cognition). Participants were assessed with the revised University of California Los Angeles (UCLA) Loneliness Scale and underwent resting-state functional MRI. Functional images were preprocessed with the CONN toolbox. The selected seeds were within brain regions reportedly associated with loneliness. One-sample general linear model analysis was performed to examine regressions of UCLA Loneliness Scale scores and functional connectivity between the seeds and regions of interest. RESULTS: The revised UCLA Loneliness Scale scores were positively correlated with functional connectivity between the right hippocampus and left lateral parietal lobe and were negatively correlated with functional connectivity between the left amygdala and left frontal operculum and between the left amygdala and right supramarginal gyrus. Analyses were adjusted for age, sex, and education and scores on the Mini-Mental State Examination and Clinical Dementia Rating scale. CONCLUSIONS: Loneliness was associated with abnormal function of the hippocampus, parts of the parietal lobe and frontal cortex, and the amygdala. These findings may suggest a possible correlation between loneliness and neurological changes associated with dementia.

Resting-state brain activity as a biomarker of chronic pain impairment and a mediator of its association with pain resilience.

Past cross-sectional chronic pain studies have revealed aberrant resting-state brain activity in regions involved in pain processing and affect regulation. However, there is a paucity of longitudinal research examining links of resting-state activity and pain resilience with changes in chronic pain outcomes over time. In this prospective study, we assessed the status of baseline (T1) resting-state brain activity as a biomarker of later impairment from chronic pain and a mediator of the relation between pain resilience and impairment at follow-up. One hundred forty-two adults with chronic musculoskeletal pain completed a T1 assessment comprising a resting-state functional magnetic resonance imaging scan based on regional homogeneity (ReHo) and self-report measures of demographics, pain characteristics, psychological status, pain resilience, pain severity, and pain impairment. Subsequently, pain impairment was reassessed at a 6-month follow-up (T2). Hierarchical multiple regression and mediation analyses assessed relations of T1 ReHo and pain resilience scores with changes in pain impairment. Higher T1 ReHo values in the right caudate nucleus were associated with increased pain impairment at T2, after controlling for all other statistically significant self-report measures. ReHo also partially mediated associations of T1 pain resilience dimensions with T2 pain impairment. T1 right caudate nucleus ReHo emerged as a possible biomarker of later impairment from chronic musculoskeletal pain and a neural mechanism that may help to explain why pain resilience is related to lower levels of later chronic pain impairment. Findings provide empirical foundations for prospective extensions that assess the status of ReHo activity and self-reported pain resilience as markers for later impairment from chronic pain and targets for interventions to reduce impairment. PRACTITIONER POINTS: Resting-state markers of impairment: Higher baseline (T1) regional homogeneity (ReHo) values, localized in the right caudate nucleus, were associated with exacerbations in impairment from chronic musculoskeletal pain at a 6-month follow-up, independent of T1 demographics, pain experiences, and psychological factors. Mediating role of ReHo values: ReHo values in the right caudate nucleus also mediated the relationship between baseline pain resilience levels and later pain impairment among participants. Therapeutic implications: Findings provide empirical foundations for research extensions that evaluate (1) the use of resting-state activity in assessment to identify people at risk for later impairment from pain and (2) changes in resting-state activity as biomarkers for the efficacy of treatments designed to improve resilience and reduce impairment among those in need.

MRI resting-state signature of the propensity to experience meaningful coincidences: a functional coupling analysis.

The propensity to experience meaningful patterns in random arrangements and unrelated events shows considerable interindividual differences. Reduced inhibitory control (over sensory processes) and decreased working memory capacities are associated with this trait, which implies that the activation of frontal as well as posterior brain regions may be altered during rest and working memory tasks. In addition, people experiencing more meaningful coincidences showed reduced gray matter of the left inferior frontal gyrus (IFG), which is linked to the inhibition of irrelevant information in working memory and the control and integration of multisensory information. To study deviations in the functional connectivity of the IFG with posterior associative areas, the present study investigated the fMRI resting state in a large sample of n = 101 participants. We applied seed-to-voxel analysis and found that people who perceive more meaningful coincidences showed negative functional connectivity of the left IFG (i.e. pars triangularis) with areas of the left posterior associative cortex (e.g. superior parietal cortex). A data-driven multivoxel pattern analysis further indicated that functional connectivity of a cluster located in the right cerebellum with a cluster including parts of the left middle frontal gyrus, left precentral gyrus, and the left IFG (pars opercularis) was associated with meaningful coincidences. These findings add evidence to the neurocognitive foundations of the propensity to experience meaningful coincidences, which strengthens the idea that deviations of working memory functions and inhibition of sensory and motor information explain why people experience more meaning in meaningless noise.

Neural correlates of impulsivity in amphetamine use disorder.

Impulsivity is a trait associated with several psychiatric conditions, not least addictive disorders. While the neural mechanisms behind certain aspects of impulsivity have been studied extensively, there are few imaging studies examining this neurocircuitry in populations with substance use disorders. Therefore, we aimed to examine the functional connectivity of relevant neural networks, and their possible association with trait impulsivity, in a sample with severe amphetamine use disorder and a control group of healthy subjects. We used data collected in a randomized clinical trial studying the acute effects of oral naltrexone in amphetamine use disorder. Our final sample included 32 amphetamine users and 27 healthy controls. Trait impulsivity was rated with the Barratt Impulsiveness Scale-11, and functional connectivity was measured during resting-state fMRI, looking specifically at networks involving prefrontal regions previously implicated in studies of impulsivity. Amphetamine users had higher subjective ratings of impulsivity as compared to healthy controls, and these scores correlated positively with a wide-spread prefrontal hyperconnectivity that was found among the amphetamine users. These findings highlight the importance of aberrant prefrontal function in severe addiction.

Speech-language within and between network disruptions in primary progressive aphasia variants.

Primary progressive aphasia (PPA) variants present with distinct disruptions in speech-language functions with little known about the interplay between affected and spared regions within the speech-language network and their interaction with other functional networks. The Neurodegenerative Research Group, Mayo Clinic, recruited 123 patients with PPA (55 logopenic (lvPPA), 44 non-fluent (nfvPPA) and 24 semantic (svPPA)) who were matched to 60 healthy controls. We investigated functional connectivity disruptions between regions within the left-speech-language network (Broca, Wernicke, anterior middle temporal gyrus (aMTG), supplementary motor area (SMA), planum temporale (PT) and parietal operculum (PO)), and disruptions to other networks (visual association, dorsal-attention, frontoparietal and default mode networks (DMN)). Within the speech-language network, multivariate linear regression models showed reduced aMTG-Broca connectivity in all variants, with lvPPA and nfvPPA findings remaining significant after Bonferroni correction. Additional loss in Wernicke-Broca connectivity in nfvPPA, Wernicke-PT connectivity in lvPPA and greater aMTG-PT connectivity in svPPA were also noted. Between-network connectivity findings in all variants showed reduced aMTG-DMN and increased aMTG-dorsal-attention connectivity, with additional disruptions between aMTG-visual association in both lvPPA and svPPA, aMTG-frontoparietal in lvPPA, and Wernicke-DMN breakdown in svPPA. These findings suggest that aMTG connectivity breakdown is a shared feature in all PPA variants, with lvPPA showing more extensive connectivity disruptions with other networks.