Alterations in functional brain connectivity associated with developmental dyscalculia.

BACKGROUND AND PURPOSE: In recent years, there has been a growing interest in the study of resting neural networks in different neurological and mental disorders. While previous studies suggest that the default mode network (DMN) may be altered in dyscalculia, the study of resting-state networks in the development of numerical skills, especially in children with developmental dyscalculia (DD), is scarce and relatively recent. Based on this, this study examines differences in resting-state functional connectivity (rs-FC) data of children with DD using functional connectivity multivariate pattern analysis (fc-MVPA), a data-driven methodology that summarizes properties of the entire connectome. METHODS: We performed fc-MVPA on resting-state images of a sample composed of a group of children with DD (n = 19, 8.06 ± 0.87 years) and an age- and sex-matched control group of typically developing children (n = 23, 7.76 ± 0.46 years). RESULTS: Analysis of fc-MVPA showed significant differences between group connectivity profiles in two clusters allocated in both the right and left medial temporal gyrus. Post hoc effect size results revealed a decreased rs-FC between each temporal pole and the DMN in children with DD and an increased rs-FC between each temporal pole and the sensorimotor network. CONCLUSIONS: Our results suggest an aberrant information flow between resting-state networks in children with DD, demonstrating the importance of these networks for arithmetic development.

Hybrid similarity based feature selection and cascade deep maxout fuzzy network for Autism Spectrum Disorder detection using EEG signal.

Autism Spectrum Disorder (ASD) is a neurological disorder that influences a person's comprehension and way of behaving. It is a lifetime disability that cannot be completely treated using any therapy up to date. Nevertheless, in time identification and continuous therapies have a huge effect on autism patients. The existing models took a long time to confirm the diagnosis process and also, it is highly complex to differentiate autism from various developmental disorders. To facilitate early diagnosis by providing timely intervention, saving healthcare costs and reducing stress for the family in the long run, this research introduces an affordable and straightforward diagnostic model to detect ASD using EEG and deep learning models. Here, a hybrid deep learning model called Cascade deep maxout fuzzy network (Cascade DMFN) is proposed to identify ASD and it is achieved by the integration of Deep Maxout Network (DMN) and hybrid cascade neuro-fuzzy. Moreover, hybrid similarity measures like Canberra distance and Kumar-hassebrook is employed to conduct the feature selection technique. Also, the EEG dataset and BCIAUT_P300 dataset are used for analyzing the designed Cascade DMFN for detecting Autism Spectrum Disorder. The designed Cascade DMFN has outperformed other classical models by yielding a high accuracy of 0.930, Negative Predictive Value (NPV) of 0.919, Positive Predictive Value (PPV) of 0.923, True Negative Rate (TNR) of 0.926, and True Positive Rate (TPR) of 0.934.

Decreased functional connectivity is associated with increased levels of Cerebral Spinal Fluid soluble-PDGFRß, a marker of blood brain barrier breakdown, in older adults.

Resting-state functional connectivity (FC) is suggested to be cross-sectionally associated with both vascular burden and Alzheimer's disease (AD) pathology. For instance, studies in pre-clinical AD subjects have shown increases of cerebral spinal fluid soluble platelet-derived growth factor receptor-ß (CSF sPDGFRß, a marker of BBB breakdown) but have not demonstrated if this vascular impairment affects neuronal dysfunction. It's possible that increased levels of sPDGFRß in the CSF may correlate with impaired FC in metabolically demanding brain regions (i.e. Default Mode Network, DMN). Our study aimed to investigate the relationship between these two markers in older individuals that were cognitively normal and had cognitive impairment. Eighty-nine older adults without dementia from the University of Southern California were selected from a larger cohort. Region of interest (ROI) to ROI analyses were conducted using DMN seed regions. Linear regression models measured significant associations between BOLD FC strength among seed-target regions and sPDGFRß values, while covarying for age and sex. Comparison of a composite ROI created by averaging FC values between seed and all target regions among cognitively normal and impaired individuals was also examined. Using CSF sPDGFRß as a biomarker of BBB breakdown, we report that increased breakdown correlated with decreased functional connectivity in DMN areas, specifically the PCC, and while the hippocampus exhibited an interaction effect using CDR score, this was an exploratory analysis that we feel can lead to further research. Ultimately, we found that BBB breakdown, as measured by CSF sPDGFRß, is associated with neural networks, and decreased functional connections.

'Remote inhibition' of motor cortex in Epileptic encephalopathy with spike-wave activation in sleep (EE-SWAS): A TMS based cortical excitability study.

OBJECTIVES: The study compared real-time motor cortex excitability using transcranial magnetic stimulation (TMS)-derived parameters between children with epileptic encephalopathy with spike-wave activation in sleep (EE-SWAS) and age-matched neurotypical controls. The EE-SWAS group received steroids as standard of care and were longitudinally followed for three months. MATERIALS & METHODS: Children aged 5-12 years with immunotherapy-naive EE-SWAS (spike-wave-index≥50 %) and neurotypical controls were enrolled. Cognitive and behavioral assessments were performed using valid psychometric tools. Real-time motor cortex excitability was assessed by measuring resting motor threshold (RMT), short intra-cortical inhibition (SICI) and long intra-cortical inhibition (LICI) in both groups. In EE-SWAS group, a follow up evaluation with TMS at 4- and 12-week intervals, EEG, and neurobehavioral assessments at 12-weeks were performed to assess the effect of steroids on cortical excitability and to determine electroclinical outcome. RESULTS: Forty-eight children with suspected EE-SWAS and 26 neurotypical controls were screened; 20 were enrolled in each group. Children with EE-SWAS (mean age: 8.05 ± 1.76 years) had cognitive and behavioral problems (20/20), and ongoing seizures (12/20). At baseline, the dominant motor cortex was significantly inhibited in the EE-SWAS group compared to neurotypical children{RMT(%)[86.3 ± 6.96 vs 58.05 ± 4.71(p < 0.0001)]; LICI(%)[55.05 ± 4.39 vs 73.9 ± 3.75(p < 0.0001)]; SICI(%)[39.2 ± 4.36 vs 55.45 ± 4.78(p < 0.0001)]}. Reversal of motor cortex inhibition was sequentially observed in EE-SWAS group at 4- and 12-week follow-ups{(RMT[4, 12 weeks]: 71.45 ± 9.83, 63.45 ± 8.48); (LICI[4, 12 weeks]: 66.00 ± 6.26, 74.50 ± 5.36); (SICI[4, 12 weeks]: 49.35 ± 6.24, 56.05 ± 5.57)}[repeated-measures ANOVA: p < 0.0001]. CONCLUSION: Motor cortex is remotely inhibited in EE-SWAS, which may contribute to neurobehavioral impairment. Steroids can disinhibit/reverse the epilepsy-induced motor cortex inhibition leading to improvement in neurobehavior.

Default mode network aberrance in subjects of alcohol and opioid use disorders during working memory task: An exploratory EEG microstates study.

Background: Aberrance in switching from default mode network (DMN) to fronto-parietal network (FPN) is proposed to underlie working memory deficits in subjects with substance use disorders, which can be studied using neuro-imaging techniques during cognitive tasks. The current study used EEG to investigate pre-stimulus microstates during the performance of Sternberg's working memory task in subjects with substance use disorders. Methods: 128-channel EEG was acquired and processed in ten age and gender-matched subjects, each with alcohol use disorder, opioid use disorder, and controls while they performed Sternberg's task. Behavioral parameters, pre-stimulus EEG microstate, and underlying sources were analyzed and compared between subjects with substance use disorders and controls. Results: Both alcohol and opioid use disorder subjects had significantly lower accuracy (P < 0.01), while reaction times were significantly higher only in subjects of alcohol use disorder compared to controls (P < 0.01) and opioid use disorder (P < 0.01), reflecting working memory deficits of varying degrees in subjects with substance use disorders. Pre-stimulus EEG microstate revealed four topographic Maps 1-4: subjects of alcohol and opioid use disorder showing significantly lower mean duration of Map 3 (visual processing) and Map 2 (saliency and DMN switching), respectively, compared to controls (P < 0.05). Conclusion: Reduced mean durations in Map 3 and 2 in subjects of alcohol and opioid use disorder can underlie their poorer performance in Sternberg's task. Furthermore, cortical sources revealed higher activity in both groups of substance use disorders in the parahippocampal gyrus- a hub of DMN; superior and middle temporal gyri associated with impulsivity; and insula that maintains balance between executive reflective system and impulsive system. EEG microstates can be used to envisage neural underpinnings implicated for working memory deficits in subjects of alcohol and opioid use disorders, reflected by aberrant switching between neural networks and information processing mechanisms.

Aberrance in default mode and executive network activity underlie working memory deficits in addictive disorders: A preliminary, exploratory case-control study.

BACKGROUND: Addiction can alter neural processes during rest and cognitive performance. Subjects with addictive disorders exhibit preoccupation and anticipation for the psychoactive substance when idle and cognitive deficits, during tasks. METHODS: 128 channel EEG was recorded in sixty subjects (30, with alcohol, opioid and internet addiction; 30 controls) during rest and while performing working memory task to ascertain underlying differences in cortical activity between the groups while at rest and during performance of the task. Artifactually clean data was then subjected to source analysis using sLORETA software in both the groups. RESULTS: EEG cortical source analysis in subjects with addictive disorders showed significant activation of areas of Default Mode Network (DMN) and reduced activation in dorsolateral prefrontal cortices (DLPFC), an area known to be involved in executive function, during performance of task. However, control subjects demonstrated significantly reduced activation in areas of DMN; and increased activation of DLPFC during task performance. CONCLUSION: Inability to suppress DMN inhibits reallocation of neural resources to areas of executive functioning leading to working memory deficits in subjects with addictive disorder.

Phenotype-Genotype Correlation of a Cohort of Patients with Congenital Myopathy: A Single Centre Experience from India.

Background: Congenital myopathies (CMs) are a diverse group of inherited muscle disorders with broad genotypic and phenotypic heterogeneity. While the literature on CM is available from European countries, comprehensive data from the Indian subcontinent is lacking. Objectives: This study aims to describe the clinical and histopathological characteristics of a cohort of genetically confirmed CMs from India and attempts to do phenotype-genotype correlation. Methods: A retrospective chart review of genetically confirmed CMs was evaluated between January 2016 and December 2020 at the neuromuscular clinic. The clinical, genetic, and follow-up data were recorded in a pre-structured proforma as per the medical records, and the data was analyzed. Results: A total of 31(M: F = 14 : 17) unrelated patients were included. The median age at onset and duration of illness are 2.0(IQR:1-8) years and 6.0(IQR:3-10) years respectively. Clinical features observed were proximodistal weakness (54.8%), facial weakness (64.5%), and myopathic facies (54.8%), followed by ptosis (33.3%), and ophthalmoplegia (19.4%). Muscle histopathology was available in 38.7% of patients, and centronuclear myopathy was the most common histopathology finding. The pathogenic genetic variants were identified in RYR1 (29.0%), DNM2 (19.4%), SELENON (12.9%), KBTBD13 (9.7%), NEB (6.5%), and MYPN (6.5%) genes. Novel mutations were observed in 30.3% of the cohort. Follow-up details were available in 77.4% of children, and the median duration of follow-up and age at last follow-up was 4.5 (Range 0.5-11) years and 13 (Range 3-35) years, respectively. The majority were ambulant with minimal assistance at the last follow-up. Mortality was noted in 8.3% due to respiratory failure in Centronuclear myopathy 1 and congenital myopathy 3 with rigid spines (SELENON). Conclusion: This study highlights the various phenotypes and patterns of genetic mutations in a cohort of pediatric patients with congenital myopathy from India. Centronuclear myopathy was the most common histological classification and the mutations in RYR1 followed by DNM2 gene were the common pathogenic variants identified. The majority were independent in their activities of daily living during the last follow-up, highlighting the fact that the disease has slow progression irrespective of the genotype.

The Complex Role Played by the Default Mode Network during Sexual Stimulation: A Cluster-Based fMRI Meta-Analysis.

The default mode network (DMN) is a complex network that plays a significant and active role during naturalistic stimulation. Previous studies that have used naturalistic stimuli, such as real-life stories or silent or sonorous films, have found that the information processing involved a complex hierarchical set of brain regions, including the DMN nodes. The DMN is not involved in low-level features and is only associated with high-level content-related incoming information. The human sexual experience involves a complex set of processes related to both external context and inner processes. Since the DMN plays an active role in the integration of naturalistic stimuli and aesthetic perception with beliefs, thoughts, and episodic autobiographical memories, we aimed at quantifying the involvement of the nodes of the DMN during visual sexual stimulation. After a systematic search in the principal electronic databases, we selected 83 fMRI studies, and an ALE meta-analysis was calculated. We performed conjunction analyses to assess differences in the DMN related to stimulus modalities, sex differences, and sexual orientation. The results show that sexual stimulation alters the topography of the DMN and highlights the DMN's active role in the integration of sexual stimuli with sexual schemas and beliefs.

Graph analysis uncovers an opposing impact of methylphenidate on connectivity patterns within default mode network sub-divisions.

BACKGROUND: The Default Mode Network (DMN) is a central neural network, with recent evidence indicating that it is composed of functionally distinct sub-networks. Methylphenidate (MPH) administration has been shown before to modulate impulsive behavior, though it is not yet clear whether these effects relate to MPH-induced changes in DMN connectivity. To address this gap, we assessed the impact of MPH administration on functional connectivity patterns within and between distinct DMN sub-networks and tested putative relations to variability in sub-scales of impulsivity. METHODS: Fifty-five right-handed healthy adults underwent two resting-state functional MRI (rs-fMRI) scans, following acute administration of either MPH (20 mg) or placebo, via a randomized double-blind placebo-controlled design. Graph modularity analysis was implemented to fractionate the DMN into distinct sub-networks based on the impact of MPH (vs. placebo) on DMN connectivity patterns with other neural networks. RESULTS: MPH administration led to an overall decreased DMN connectivity, particularly with the auditory, cinguloopercular, and somatomotor networks, and increased connectivity with the parietomedial network. Graph analysis revealed that the DMN could be fractionated into two distinct sub-networks, with one exhibiting MPH-induced increased connectivity and the other decreased connectivity. Decreased connectivity of the DMN sub-network with the cinguloopercular network following MPH administration was associated with elevated impulsivity and non-planning impulsiveness. CONCLUSION: Current findings highlight the intricate effects of MPH administration on DMN rs-fMRI connectivity, uncovering its opposing impact on distinct DMN sub-divisions. MPH-induced dynamics in DMN connectivity patterns with other neural networks may account for some of the effects of MPH administration on impulsive behavior.

Comparative analysis of resting-state EEG functional connectivity in depression and obsessive-compulsive disorder.

Major depressive disorder (MDD) and obsessive-compulsive disorder (OCD) are psychiatric disorders that often co-occur. We aimed to investigate whether their high comorbidity could be traced not only by clinical manifestations, but also at the level of functional brain activity. In this paper, we examined the differences in functional connectivity (FC) at the whole-brain level and within the default mode network (DMN). Resting-state EEG was obtained from 43 controls, 26 OCD patients, and 34 MDD patients. FC was analyzed between 68 cortical sources, and between-group differences in the 4-30 Hz range were assessed via the Network Based Statistic method. The strength of DMN intra-connectivity was compared between groups in the theta, alpha and beta frequency bands. A cluster of 67 connections distinguished the OCD, MDD and control groups. The majority of the connections, 8 of which correlated with depressive symptom severity, were found to be weaker in the clinical groups. Only 3 connections differed between the clinical groups, and one of them correlated with OCD severity. The DMN strength was reduced in the clinical groups in the alpha and beta bands. It can be concluded that the high comorbidity of OCD and MDD can be traced at the level of FC.

Altered dynamic functional connectivity of motor cerebellum with sensorimotor network and default mode network in juvenile myoclonic epilepsy.

Objective: To investigate whether changes occur in the dynamic functional connectivity (dFC) of motor cerebellum with cerebral cortex in juvenile myoclonic epilepsy (JME). Methods: We adopted resting-state electroencephalography-functional magnetic resonance imaging (EEG-fMRI) and a sliding-window approach to explore the dFC of motor cerebellum with cortex in 36 JME patients compared with 30 and age-matched health controls (HCs). The motor cerebellum was divided into five lobules (I-V, VI, VIIb, VIIIa, and VIIIb). Additionally, correlation analyses were conducted between the variability of dFC and clinical variables in the Juvenile Myoclonic Epilepsy (JME) group, such as disease duration, age at disease onset, and frequency score of myoclonic seizures. Results: Compared to HCs, the JME group presented increased dFC between the motor cerebellum with SMN and DMN. Specifically, connectivity between lobule VIIb and left precentral gyrus and right inferior parietal lobule (IPL); between lobule VIIIa and right inferior frontal gyrus (IFG) and left IPL; and between lobule VIIIb and left middle frontal gyrus (MFG), bilateral superior parietal gyrus (SPG), and left precuneus. In addition, within the JME group, the strength of dFC between lobule VIIIb and left precuneus was negatively (r = -0.424, p = 0.025, Bonferroni correction) related with the frequency score of myoclonic seizures. Conclusion: In patients with JME, there is a functional dysregulation between the motor cerebellum with DMN and SMN, and the variability of dynamic functional connectivity may be closely associated with the occurrence of motor symptoms in JME.

Advancing Alzheimer's research: Radiomics visualization of the default mode network in cerebral perfusion imaging.

OBJECTIVE: Alzheimer's disease, an irreversible neurological condition, demands timely diagnosis for effective clinical intervention. This study employs radiomics analysis to assess image features in default mode network cerebral perfusion imaging among individuals with cognitive impairment. METHODS: A radiomics analysis of cerebral perfusion imaging was conducted on 117 patients with cognitive impairment. They were divided into training and validation sets in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest were employed to select and model image features, followed by logistic regression analysis of LASSO and Random Forest results. Diagnostic performance was assessed by calculating the area under the curve (AUC). RESULTS: In the training set, LASSO achieved AUC of 0.978, Random Forest had an AUC of 0.933. In the validation set, LASSO had AUC of 0.859, Random Forest had AUC of 0.986. By conducting Logistic Regression analysis in combination with LASSO and Random Forest, we identified a total of five radiomics features, with four related to morphology and one to textural features, originating from the medial prefrontal cortex and middle temporal gyrus. In the training set, Logistic Regression achieved AUC of 0.911, while in the validation set, it attained AUC of 0.925. CONCLUSION: The medial prefrontal cortex and middle temporal gyrus are the two brain regions within the default mode network that hold the highest significance for Alzheimer's disease diagnosis. Radiomics analysis contributes to the clinical assessment of Alzheimer's disease by delving into image data to extract deeper layers of information.

Rest to Promote Learning: A Brain Default Mode Network Perspective.

The brain often switches freely between focused attention and divergent thinking, and the Default Mode Network (DMN) is activated during brain rest. Since its discovery, the DMN, together with its function and characteristics, indicates that learning does not stop when the brain "rests". Therefore, DMN plays an important role in learning. Neural activities such as beta wave rhythm regulation, "subconscious" divergence thinking mode initiation, hippocampal function, and neural replay occur during default mode, all of which explains that "rest" promotes learning. This paper summarized the function and neural mechanism of DMN in learning and proposed that the DMN plays an essential role in learning, which is that it enables rest to promote learning.

A prospective study on EEG default mode network associated with subsequent posttraumatic stress disorder following sexual assault.

This study aimed to explore the predictors of posttraumatic stress disorder (PTSD) in women who have recently experienced sexual assault, by examining psychological and neurophysiological factors using a prospective design with resting-state electroencephalogram (EEG) functional connectivity. The study enrolled 33 women who had been recently traumatized by sexual assault and conducted assessments within a month of the trauma. These survivors were evaluated for PTSD three months later and were classified into two groups: PTSD positive (n = 12) and PTSD negative (n = 21). They were compared to two control groups comprising women who had not experienced any extremely traumatic events: 25 with depression and 25 healthy controls. The evaluation focused on resting-state EEG functional connectivity within default mode network (DMN) using small-worldness (SW), based on graph theory. We also assessed self-reported levels of depression, anxiety, anger, and executive functions. The findings indicated that survivors who developed PTSD three months post-trauma exhibited higher anxiety levels and reduced DMN SW in the beta 3 frequency, compared to those who did not develop PTSD. Contrary to expectations, survivors without PTSD showed decreased executive functioning and lower prefrontal centrality compared to those with PTSD. This study underscores the importance of early assessment and intervention for sexual assault survivors at risk of developing PTSD.

Decision discovery using clinical decision support system decision log data for supporting the nurse decision-making process.

BACKGROUND: Decision-making in healthcare is increasingly complex; notably in hospital environments where the information density is high, e.g., emergency departments, oncology departments, and psychiatry departments. This study aims to discover decisions from logged data to improve the decision-making process. METHODS: The Design Science Research Methodology (DSRM) was chosen to design an artifact (algorithm) for the discovery and visualization of decisions. The DSRM's different activities are explained, from the definition of the problem to the evaluation of the artifact. During the design and development activities, the algorithm itself is created. During the demonstration and evaluation activities, the algorithm was tested with an authentic synthetic dataset. RESULTS: The results show the design and simulation of an algorithm for the discovery and visualization of decisions. A fuzzy classifier algorithm was adapted for (1) discovering decisions from a decision log and (2) visualizing the decisions using the Decision Model and Notation standard. CONCLUSIONS: In this paper, we show that decisions can be discovered from a decision log and visualized for the improvement of the decision-making process of healthcare professionals or to support the periodic evaluation of protocols and guidelines.

Strengthening through adversity: The hormesis model in developmental psychopathology.

BACKGROUND: Employing a developmental psychopathology framework, we tested the utility of the hormesis model in examining the strengthening of children and youth through limited levels of adversity in relation to internalizing and externalizing outcomes within a brain-by-development context. METHODS: Analyzing data from the Adolescent Brain and Cognitive Development study (N = 11,878), we formed latent factors of threat, deprivation, and unpredictability. We examined linear and nonlinear associations between adversity dimensions and youth psychopathology symptoms and how change of resting-state functional connectivity (rsFC) in the default mode network (DMN) from Time 1 to Time 5 moderates these associations. RESULTS: A cubic association was found between threat and youth internalizing problems; low-to-moderate family conflict levels reduced these problems. Deprivation also displayed a cubic relation with youth externalizing problems, with moderate deprivation levels associated with fewer problems. Unpredictability linearly increased both problem types. Change in DMN rsFC significantly moderated the cubic link between threat levels and internalizing problems, with declining DMN rsFC levels from Time 1 to Time 5 facilitating hormesis. Hormetic effects peaked earlier, emphasizing the importance of sensitive periods and developmental timing of outcomes related to earlier experiences. CONCLUSIONS: Strengthening through limited environmental adversity is crucial for developing human resilience. Understanding this process requires considering both linear and nonlinear adversity-psychopathology associations. Testing individual differences by brain and developmental context will inform preventive intervention programming.

Trait anxiety and corresponding neuromarkers predict internet addiction: A longitudinal study.

Background and Aims: The high prevalence of internet addiction (IA) has become a worldwide problem that profoundly affects people's mental health and executive function. Empirical studies have suggested trait anxiety (TA) as one of the most robust predictors of addictive behaviors. The present study investigated the neural and socio-psychological mechanisms underlying the association between TA and IA. Methods: Firstly, we tested the correlation between TA and IA. Then we investigated the longitudinal influence of TA on IA using a linear mixed effect (LME) model. Secondly, connectome-based predictive modeling (CPM) was employed to explore neuromarkers of TA, and we tested whether the identified neuromarkers of TA can predict IA. Lastly, stressful life events and default mode network (DMN) were considered as mediating variables to explore the relationship between TA and IA. Findings: A significant positive correlation between TA and IA was found and the high TA group demonstrated higher IA across time. CPM results revealed that the functional connectivity of cognitive control and emotion-regulation circuits and DMN were significantly correlated with TA. Furthermore, a significant association was found between the neuromarkers of TA and IA. Notably, the CPM results were all validated in an independent sample. The results of mediation demonstrated that stressful life events and correlated functional connectivity mediated the association between TA and IA. Conclusions: Findings of the present study facilitate a deeper understanding of the neural and socio-psychological mechanisms linking TA and IA and provide new directions for developing neural and psychological interventions.

Impact of sex and reproductive status on the default mode network in early midlife: implications for aging of memory circuitry and function.

Alterations to the resting-state default mode network (rsDMN) are early indicators of memory decline and Alzheimer's disease (AD). Brain regions shared by the rsDMN and memory circuitry are highly sexually dimorphic. However, data are limited regarding the impact of sex and reproductive status on rsDMN connectivity and memory circuitry and function. In the current investigation, rsDMN connectivity was assessed in 180 early midlife adults aged 45 to 55 by sex and reproductive status (87 women; 93 men). Associations between left and right hippocampal connectivity of rsDMN and verbal memory encoding circuitry were examined using linear mixed models, controlled for age and parental socioeconomic status, testing interactions by sex and reproductive status. Relative to men, women exhibited greater rsDMN connectivity between the left and right hippocampus. In relation to rsDMN-memory encoding connectivity, sex differences were revealed across the menopausal transition, such that only postmenopausal women exhibited loss of the ability to decrease rsDMN left-right hippocampal connectivity during memory encoding associated with poorer memory performance. Results demonstrate that sex and reproductive status play an important role in aging of the rsDMN and interactions with memory circuitry/function. This suggests the critical importance of sex and reproductive status when studying early midlife indicators of memory decline and AD risk.

Altered Neuromagnetic Activity in the Default Mode Network in Migraine and Its Subgroups (Episodic Migraine and Chronic Migraine).

BACKGROUND: The differences in the resting state spectral power and functional connectivity of the default mode network between people with migraine without aura (MwoA) and its subgroups differentiated by frequency (episodic migraine (EM) and chronic migraine (CM)) and healthy controls (HC) were investigated using magnetoencephalography. METHODS: In the resting state, the topological spatial structure of the brain in 33 MwoA patients and 22 HC was first studied using magnetoencephalography, followed by probing the neuroelectrical activity of 17 CM and 16 EM patients, to identify damage to their default mode network (DMN). The techniques used to investigate both spectral power and functional connectivity were minimum-paradigm estimation combined with Welch's technique and corrected amplitude envelope correlation. RESULTS: The differences between MwoA and its subgroups (CM and EM) and HC based on spectral power were mainly in the delta, theta, and alpha bands, while the differences in functional connectivity were primarily in the delta, alpha, and beta bands. In the delta and theta bands, the spectral power of MwoA and its subgroups (CM and EM) was higher than in the HC group. The spectral power of MwoA and its subgroups (CM and EM) was lower in the alpha band. In terms of functional connectivity, the corrected amplitude envelope correlation of MwoA and its subgroups (CM and EM) was lower than the HC group in the bands with spectral differences. People with EM and CM differed in the spectral power in the left medial prefrontal cortex and the right lateral temporal cortex in the alpha band, where correlation analysis and logistic regression analysis showed that the intensity of the spectral power of the left medial prefrontal cortex was negatively correlated with headache frequency. CONCLUSIONS: The spectral power of the left medial prefrontal cortex in the alpha band may serve as a biomarker that is associated with the number of monthly headache attacks and may be a potential neuromodulatory target for controlling migraine chronicity.

Dual-Process Theory of Thought and Inhibitory Control: An ALE Meta-Analysis.

The dual-process theory of thought rests on the co-existence of two different thinking modalities: a quick, automatic, and associative process opposed to a slow, thoughtful, and deliberative process. The increasing interest in determining the neural foundation of the dual-process distinction has yielded mixed results, also given the difficulty of applying the fMRI standard approach to tasks usually employed in the cognitive literature. We report an activation likelihood estimation (ALE) meta-analysis to investigate the neural foundation of the dual-process theory of thought. Eligible studies allowed for the identification of cerebral areas associated with dual-process theory-based tasks without differentiating between fast and slow thinking. The ALE algorithm converged on the medial frontal cortex, superior frontal cortex, anterior cingulate cortex, insula, and left inferior frontal gyrus. These structures partially overlap with the cerebral areas recurrently reported in the literature about the neural basis of the dual-process distinction, where the PARCS theory-based interpretation emphasizes the role of the right inferior gyrus. The results confirm the potential (but still almost unexplored) common ground between the dual-process literature and the cognitive control literature.