Changes In Brain Networks Research Articles

The Savant Syndrome: a Gift or a Disability? A Deeper Look into Metabolic Correlates of Hidden Cognitive Capacity.

Savant syndrome is a rare and unusual condition that occurs in the presence of severe developmental disabilities, disorders, and injuries. The syndrome can be congenital from birth to childhood or acquired as a result of a brain injury or damage to the central nervous system. There are several findings that indicate that savant syndrome usually occurs with significant brain metabolism alterations resulting in critical brain network changes. These types of changes in the brain are usually explained by the "tyranny of the left hemisphere" theory, which indicates the inhibition of the left hemisphere to allow the right hemisphere to develop savant abilities. Another way to temporarily simulate these types of changes in the brain can be through different neuromodulation techniques such as transcranial magnetic stimulation and transcranial direct current stimulation. Such neuromodulation techniques might help us discover the "hidden talent" potential through modulating the brain network metabolism. We herein discussed the types of savant syndrome along with its relation to specific neurometabolic network alterations. Furthermore, we provide a perspective on how newly developed neuromodulation and cognitive rehabilitation techniques can help simulate savant syndrome in healthy individuals through modulating the brain network activity.

A deep residual model for characterization of 5D spatiotemporal network dynamics reveals widespread spatiodynamic changes in schizophrenia.

Schizophrenia is a severe brain disorder with serious symptoms including delusions, disorganized speech, and hallucinations that can have a long-term detrimental impact on different aspects of a patient's life. It is still unclear what the main cause of schizophrenia is, but a combination of altered brain connectivity and structure may play a role. Neuroimaging data has been useful in characterizing schizophrenia, but there has been very little work focused on voxel-wise changes in multiple brain networks over time, despite evidence that functional networks exhibit complex spatiotemporal changes over time within individual subjects. Recent studies have primarily focused on static (average) features of functional data or on temporal variations between fixed networks; however, such approaches are not able to capture multiple overlapping networks which change at the voxel level. In this work, we employ a deep residual convolutional neural network (CNN) model to extract 53 different spatiotemporal networks each of which captures dynamism within various domains including subcortical, cerebellar, visual, sensori-motor, auditory, cognitive control, and default mode. We apply this approach to study spatiotemporal brain dynamism at the voxel level within multiple functional networks extracted from a large functional magnetic resonance imaging (fMRI) dataset of individuals with schizophrenia (N = 708) and controls (N = 510). Our analysis reveals widespread group level differences across multiple networks and spatiotemporal features including voxel-wise variability, magnitude, and temporal functional network connectivity in widespread regions expected to be impacted by the disorder. We compare with typical average spatial amplitude and show highly structured and neuroanatomically relevant results are missed if one does not consider the voxel-wise spatial dynamics. Importantly, our approach can summarize static, temporal dynamic, spatial dynamic, and spatiotemporal dynamics features, thus proving a powerful approach to unify and compare these various perspectives. In sum, we show the proposed approach highlights the importance of accounting for both temporal and spatial dynamism in whole brain neuroimaging data generally, shows a high-level of sensitivity to schizophrenia highlighting global but spatially unique dynamics showing group differences, and may be especially important in studies focused on the development of brain-based biomarkers.

The association between genetic variations and morphology-based brain networks changes in Alzheimer's disease.

Alzheimer's disease (AD) is a highly heritable disease. The morphological changes of cortical cortex (such as, cortical thickness and surface area) in AD always accompany by the change of the functional connectivity to other brain regions and influence the short- and long-range brain network connections, causing functional deficits of AD. In this study, the first hypothesis is that genetic variations might affect morphology-based brain networks, leading to functional deficits; the second hypothesis is that protein-protein interaction (PPI) between the candidate proteins and known interacting proteins to AD might exist and influence AD. 600 470 variants and structural magnetic resonance imaging scans from 175 AD patients and 214 healthy controls were obtained from the Alzheimer's Disease Neuroimaging Initiative-1 database. A co-sparse reduced-rank regression model was fit to study the relationship between non-synonymous mutations and morphology-based brain networks. After that, PPIs between selected genes and BACE1, an enzyme that was known to be related to AD, are explored by using molecular dynamics (MD) simulation and co-immunoprecipitation (Co-IP) experiments. Eight genes affecting morphology-based brain networks were identified. The results of MD simulation showed that the PPI between TGM4 and BACE1 was the strongest among them and its interaction was verified by Co-IP. Hence, gene variations influence morphology-based brain networks in AD, leading to functional deficits. This finding, validated by MD simulation and Co-IP, suggests that the effect is robust.

Study of brain network alternations in non-lesional epilepsy patients by BOLD-fMRI.

To investigate the changes of brain network in epilepsy patients without intracranial lesions under resting conditions. Twenty-six non-lesional epileptic patients and 42 normal controls were enrolled for BOLD-fMRI examination. The differences in brain network topological characteristics and functional network connectivity between the epilepsy group and the healthy controls were compared using graph theory analysis and independent component analysis. The area under the curve for local efficiency was significantly lower in the epilepsy patients compared with healthy controls, while there were no differences in global indicators. Patients with epilepsy had higher functional connectivity in 4 connected components than healthy controls (orbital superior frontal gyrus and medial superior frontal gyrus, medial superior frontal gyrus and angular gyrus, superior parietal gyrus and paracentral lobule, lingual gyrus, and thalamus). In addition, functional connectivity was enhanced in the default mode network, frontoparietal network, dorsal attention network, sensorimotor network, and auditory network in the epilepsy group. The topological characteristics and functional connectivity of brain networks are changed in in non-lesional epilepsy patients. Abnormal functional connectivity may suggest reduced brain efficiency in epilepsy patients and also may be a compensatory response to brain function early at earlier stages of the disease.

Construction and analysis of functional network of hemi-brain in patients with brain tumors before and after anesthesia based on resting-state functional magnetic resonance imaging

Objective: To construct and analyze the functional network changes of hemi-brain in patients with brain tumor before and after anesthesia by using resting state functional magnetic resonance imaging (rs-fMRI). Methods: A total of 18 right-handed patients were prospectively included (6 males and 12 females). The patients underwent glioma resection in Peking University International Hospital from December 2018 to December 2021, and age ranged from 20 to 65 (45.1±13.6) years, with American Society of Anesthesiologists (ASA) grade of Ⅰ-Ⅱ. MRI scans were performed while the patient was awake and at the depth of surgical anesthesia. The functional network of healthy lateral brain was constructed and analyzed by means of graph theory, and its global and local topological properties were calculated. Global topology attributes included global efficiency (Eg), local efficiency (Eloc), clustering parameters (Cp), length parameter of shortest path (Lp), and small world (SW). Topology attributes of nodes included node degree (ND), node efficiency (NE) and between centrality (BC). The global and nodal topological properties of the hemi-brain network were compared between patients with different hemispherical space occupying under wakefulness and anesthesia. Results: At the awake state, Eloc and Cp in the global topological attributes of hemi-brain network were 0.259±0.007 and 0.197±0.010, respectively, and decreased to 0.242±0.013 and 0.177±0.021, respectively after anesthesia, with statistically significant differences (all P<0.01). The topological attributes of the nodes in hemi-side brain showed that ND, NE and BC were increased in the default mode network-related brain regions, while NE and BC were decreased in the limbic system and subcortical structures. Eloc and Cp were 0.258±0.008 and 0.198±0.008 respectively in the patients with left hemisphere space occupying, and decreased to 0.241±0.011 and 0.177±0.015 respectively after anesthesia, with statistically significant differences (all P<0.01). However, only Eloc decreased in patients with right hemisphere space occupying after anesthesia, and Eloc was 0.260±0.006 and 0.243±0.016 respectively when awake and after anesthesia, with statistically significant differences (P<0.05). The topological attributes of nodes in patients with space occupying in different cerebral hemispheres showed bidirectional changes after anesthesia, and patients with space occupying in the left cerebral hemisphere were more likely to be widely affected after anesthesia. The effects of anesthetic drugs may show hemispheric laterality. If the tumor was in the dominant hemisphere, the compensatory function of the dominant side was more likely to be damaged. Conclusions: During anesthesia-induced loss of consciousness in patients with brain tumors, both the ability to integrate information and the functional connections between local regions are weakened, and some brain regions have functional connection reorganization. The changes of brain network after anesthesia are bidirectional regulation.

Exploring Adaptive Graph Topologies and Temporal Graph Networks for EEG-Based Depression Detection.

In recent years, Graph Neural Networks (GNNs) based on deep learning techniques have achieved promising results in EEG-based depression detection tasks but still have some limitations. Firstly, most existing GNN-based methods use pre-computed graph adjacency matrices, which ignore the differences in brain networks between individuals. Additionally, methods based on graph-structured data do not consider the temporal dependency information of brain networks. To address these issues, we propose a deep learning algorithm that explores adaptive graph topologies and temporal graph networks for EEG-based depression detection. Specifically, we designed an Adaptive Graph Topology Generation (AGTG) module that can adaptively model the real-time connectivity of the brain networks, revealing differences between individuals. In addition, we designed a Graph Convolutional Gated Recurrent Unit (GCGRU) module to capture the temporal dynamical changes of brain networks. To further explore the differential features between depressed and healthy individuals, we adopt Graph Topology-based Max-Pooling (GTMP) module to extract graph representation vectors accurately. We conduct a comparative analysis with several advanced algorithms on both public and our own datasets. The results reveal that our final model achieves the highest Area Under the Receiver Operating Characteristic Curve (AUROC) on both datasets, with values of 83% and 99%, respectively. Furthermore, we perform extensive validation experiments demonstrating our proposed method's effectiveness and advantages. Finally, we present a comprehensive discussion on the differences in brain networks between healthy and depressed individuals based on the outputs of our final model's AGTG and GTMP modules.

Formal thought disorder is associated with brain network changes across diagnoses

A new study provides evidence for a long-discussed hypothesis: For the first time, researchers were able to show that alterations in the structural networks of affective and psychotic disorder patients’ brains correlate with the patients’ symptoms and not with their diagnosis. This study thus highlights the need for transdiagnostic, syndromal approaches to mental disorders.

Dynamic analysis of epileptic causal brain networks based on directional transfer function

Epilepsy is a neurological disease with disordered brain network connectivity. It is important to analyze the brain network mechanism of epileptic seizure from the perspective of directed functional connectivity. In this paper, causal brain networks were constructed for different sub-bands of epileptic electroencephalogram (EEG) signals in interictal, preictal and ictal phases by directional transfer function method, and the information transmission pathway and dynamic change process of brain network under different conditions were analyzed. Finally, the dynamic changes of characteristic attributes of brain networks with different rhythms were analyzed. The results show that the topology of brain network changes from stochastic network to rule network during the three stage and the node connections of the whole brain network show a trend of gradual decline. The number of pathway connections between internal nodes of frontal, temporal and occipital regions increase. There are a lot of hub nodes with information outflow in the lesion region. The global efficiency in ictal stage of α, β and γ waves are significantly higher than in the interictal and the preictal stage. The clustering coefficients in preictal stage are higher than in the ictal stage and the clustering coefficients in ictal stage are higher than in the interictal stage. The clustering coefficients of frontal, temporal and parietal lobes are significantly increased. The results of this study indicate that the topological structure and characteristic properties of epileptic causal brain network can reflect the dynamic process of epileptic seizures. In the future, this study has important research value in the localization of epileptic focus and prediction of epileptic seizure.

Disrupted intrinsic functional brain network in patients with late-life depression: Evidence from a multi-site dataset

BackgroundLate-life depression (LLD) is a common and serious mental disorder, whose neural mechanisms are not yet fully understood. In this study, we aimed to characterize LLD-related changes in intrinsic functional brain networks using a large, multi-site sample. MethodsUsing resting-state functional magnetic resonance imaging, the edge-based functional connectivity (FC) as well as multiple topological brain network metrics at both global and nodal levels were compared between 206 LLD patients and 210 normal controls (NCs). ResultsCompared with NCs, the LLD patients had extensive alterations in the intrinsic brain FCs, especially significant decreases in FCs within the default mode network (DMN) and within the somatomotor network (SMN). The LLD patients also showed alterations in several global brain network metrics compared with NCs, including significant decreases in global efficiency, local efficiency, clustering coefficient, and small-worldness, as well as a significantly increased characteristic path length. Moreover, significant alterations in nodal network metrics (increased nodal betweenness and decreased nodal efficiency) were found in patients with LLD, which mainly involved the DMN and SMN. Post-hoc subgroup analyses indicated that the above changes in FC strengths were present in both first-episode, drug-naïve (FEDN) and non-FEDN patients, and were correlated with depression severity in the FEDN patients. Moreover, changes in FC strengths were found in both the early/late-onset (depression starts before/after the age of 50) patients, while altered topological metrics were found in only the late-onset patients. ConclusionsThese results may help to strengthen our understanding of the underlying neural mechanisms and biological heterogeneity in LLD.

Greater functional connectivity between the ventral frontal cortex and occipital cortex in herpes zoster patients than post-herpetic neuralgia patients.

This study aimed to compare whole brain network between herpes zoster (HZ) patients and post-herpetic neuralgia (PHN) patients, as well as to investigate the associations between whole brain network changes and pain intensity and the accuracy of classifying between different types of pain. PHN patients (n = 50) and HZ patients (n = 50) and healthy controls (HCs) (n = 50) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Functional connectivity and global and local graph theory metrics were calculated by using Dosenbach-160 atlas. The relationship between neuroimaging indicators and clinical scales was evaluated using correlation analysis, and receiver operating characteristic (ROC) curves evaluated the feasibility of classifying PHN and HZ patients using specific neuroimaging indicators. (1) 10 greater average connectivities were found in HZ group among the default mode, frontoparietal, cingulo-opercular, sensorimotor, occipital networks (ONs), and cerebellum (p < 0.001). (2) HZ patients exhibited higher global efficiency than those in the PHN and HCs (t = 2.178, p = 0.038). (3) Multiple linear regression analyses indicated that functional connectivity between the ventral frontal cortex in the cingulo-opercular network and the occipital gyrus in the ON influenced the visual analog score pain scores (β = 4.273; p = 0.004). The variation of functional connectivity between ventral frontal cortex in the cingulo-opercular network and occipital gyrus in the ON may be a robust neuroimaging marker of the transition from HZ to PHN patients. Whole-brain network analysis may be effective in distinguishing HZ and PHN patients and predicting pain intensity.

An examination of white matter integrity and functional network organization in Subjective Cognitive Decline using Diffusional Kurtosis Imaging‐based tractography resting state fMRI

AbstractBackgroundSubjective Cognitive Decline (SCD) may increase the risk of Alzheimer’s Disease and related dementias (ADRD). Brain network changes may occur at both the functional and structural levels in SCD. To better understand these network changes, diffusional kurtosis imaging (DKI) and functional connectome (FC) measures were examined in SCD and healthy controls (HC). We hypothesized that DKI and FC measures in the medial temporal lobe would be sensitive to differences between SCD and healthy controls (HC).MethodSCD classification was determined by clinician evaluation or Everyday Cognition average item score &gt; 1.6, with objectively healthy cognitive performance (Montreal Cognitive Assessment score &gt; 22). DKI and fMRI images were acquired on a Siemens PRISMA scanner in 33 SCD subjects and 33 age/sex matched HCs (mean age=69.1 yr). DSI Studio’s Automatic fiber tracking extracted the DKI measure mean kurtosis (MK) in 3 bilateral white matter bundles associated with ADRD: inferior longitudinal fasciculus (ILF), cingulum‐parahippocampus (CP), and uncinate fasciculus (UF). FC measure eigenvector centrality (Evc) was calculated in 19 medial temporal nodes using the Brain Connectivity Toolbox. Generalized Linear Models were conducted for each bundle to determine whether EVC in the left ventrolateral amygdala (LAMG) was predicted by MK, Diagnosis or the MK*Diagnosis interaction.ResultIn the left ILF, the effect of MK approached significance (p = .054) as did the MK*Diagnosis interaction (p = .059). Lower MK was associated with higher LAMG EVC in HC (r = ‐.40) but there was no association between MK and EVC for SCD. In the left UF, effect of MK was significant (p = .003), but no effect of Diagnosis and no interaction. Lower MK was associated with higher LAMG EVC (r = ‐.359) in both SCD and HC.ConclusionThis preliminary study showed that in HC, white matter integrity of the ILF was associated with LAMG functional connectivity, but this association was absent in SCD. While present results should be interpreted with caution due to small sample sizes, these findings indicate that healthy aging structural‐functional associations are disrupted in SCD, but future studies should determine whether this is a reliable marker of brain organization changes.

Growing Brains, Nurturing Minds-Neuroscience as an Educational Tool to Support Students' Development as Life-Long Learners.

Compared to other primates, humans are late bloomers, with exceptionally long childhood and adolescence. The extensive developmental period of humans is thought to facilitate the learning processes required for the growth and maturation of the complex human brain. During the first two and a half decades of life, the human brain is a construction site, and learning processes direct its shaping through experience-dependent neuroplasticity. Formal and informal learning, which generates long-term and accessible knowledge, is mediated by neuroplasticity to create adaptive structural and functional changes in brain networks. Since experience-dependent neuroplasticity is at full force during school years, it holds a tremendous educational opportunity. In order to fulfill this developmental and learning potential, educational practices should be human-brain-friendly and "ride" the neuroplasticity wave. Neuroscience can inform educators about the natural learning mechanisms of the brain to support student learning. This review takes a neuroscientific lens to explore central concepts in education (e.g., mindset, motivation, meaning-making, and attention) and suggests two methods of using neuroscience as an educational tool: teaching students about their brain (content level) and considering the neuro-mechanisms of learning in educational design (design level).

Modeling Brain Functional Connectivity Patterns during an Isometric Arm Force Exertion Task at Different Levels of Perceived Exertion: A Graph Theoretical Approach.

The perception of physical exertion is the cognitive sensation of work demands associated with voluntary muscular actions. Measurements of exerted force are crucial for avoiding the risk of overexertion and understanding human physical capability. For this purpose, various physiological measures have been used; however, the state-of-the-art in-force exertion evaluation lacks assessments of underlying neurophysiological signals. The current study applied a graph theoretical approach to investigate the topological changes in the functional brain network induced by predefined force exertion levels for twelve female participants during an isometric arm task and rated their perceived physical comfort levels. The functional connectivity under predefined force exertion levels was assessed using the coherence method for 84 anatomical brain regions of interest at the electroencephalogram (EEG) source level. Then, graph measures were calculated to quantify the network topology for two frequency bands. The results showed that high-level force exertions are associated with brain networks characterized by more significant clustering coefficients (6%), greater modularity (5%), higher global efficiency (9%), and less distance synchronization (25%) under alpha coherence. This study on the neurophysiological basis of physical exertions with various force levels suggests that brain regions communicate and cooperate higher when muscle force exertions increase to meet the demands of physically challenging tasks.

The Institute of Physical Medicine and Rehabilitation, Hospital das Clínicas University of São Paulo School of Medicine comprehensive rehabilitation program for elderly people with knee osteoarthritis.

Knee osteoarthritis (OA) is a leading cause of disability in the elderly population. Chronic disabling pain is associated with maladaptive neuroplastic changes in brain networks, commonly associated with central sensitization. The main clinical features of nociplastic pain conditions include combined peripheral and central sensitization, and it is crucial to recognize this type of pain, as it responds to different therapies than nociceptive and neuropathic pain. To report the effect of the Institute of Physical Medicine and Rehabilitation (IMREA) comprehensive rehabilitation program to reduce pain and to improve functioning in elderly people with knee OA, under the DEFINE cohort. This is a retrospective observational cohort of 96 patients with knee OA, recruited from October 2018 to December 2019. All patients were evaluated by a trained multidisciplinary team using the Kellgren Lawrence classification, bilateral knee ultrasonography, the visual analog scale (VAS), the Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain, rigidity and difficulty scores, the Timed Up and Go Test (TUG), 10-m and 6-min walking test (10 and 6 MWT), Berg Balance Scale, isokinetic dynamometry for knee extension and flexion strength, and pain pressure thresholds. The rehabilitation program included paraspinous lidocaine blocks, focal extracorporeal shockwaves combined with radial pressure waves and functional electrical stimulation according to individual needs. The baseline was compred with the treatment results with a paired t-test. The study sample is composed of 96 participants, mostly females (n = 81, 84.38%), with bilateral osteoarthritis (n = 91, 94.79%), and a mean age of 68.89 (SD 9.73) years. Functional improvement was observed in TUG (p = 0.019), 6-mwt (p = 0.033), right knee flexion strength (p < 0.0001), WOMAC rigidity and difficulty domains (p < 0.0001). Pain was reduced from baseline as measured by WOMAC pain domain (p < 0.0001), VAS for both knees (p < 0.0001), and SF-36 pain domain (p < 0.0001). Pressure pain threshold was modified above the patella (p = 0.005 and p = 0.002 for right and left knees, respectively), at the patellar tendons (p = 0.015 and p = 0.010 for right and left patellar tendons, respectively), left S2 dermatome (p = 0.017), and L1-L2 (p = 0.008). The IMREA comprehensive rehabilitation program improved functioning and reduced disabling pain in elderly people with knee OA. We highlight the relevance and discuss the implementation of our intervention protocol. Although this is an open cohort study, it is important to note the significant improvement with this clinical protocol.

The temporal dynamics of Large-Scale brain network changes in disorders of consciousness: A Microstate-Based study.

The resting-state brain is composed of several discrete networks, which remain stable for 10-100 ms. These functional microstates are considered the building blocks of spontaneous consciousness. Electroencephalography (EEG) microstate analysis may provide insight into the altered brain dynamics underlying consciousness recovery in patients with disorders of consciousness (DOC). We aimed to analyze microstates in the resting-state EEG source space in patients with DOC, the relationship between state-specific features and consciousness levels, and the corresponding patterns of microstates and functional networks. We obtained resting-state EEG data from 84 patients with DOC (27 in a minimally conscious state [MCS] and 57 in a vegetative state [VS] or with unresponsive wakefulness syndrome). We conducted a microstate analysis of the resting-state (EEG) source space and developed a state-transition analysis protocol for patients with DOC. We identified seven microstates with distinct spatial distributions of cortical activation. Multivariate pattern analyses revealed that different functional connectivity patterns were associated with source-level microstates. There were significant differences in the microstate properties, including spatial activation patterns, temporal dynamics, state shifts, and connectivity construction, between the MCS and VS groups. Our findings suggest that consciousness depends on complex dynamics within the brain and may originate from the anterior cortex.

How Discrimination Gets Under the Skin: Biological Determinants of Discrimination Associated With Dysregulation of the Brain-Gut Microbiome System and Psychological Symptoms

BackgroundDiscrimination is associated with negative health outcomes as mediated in part by chronic stress, but a full understanding of the biological pathways is lacking. Here we investigate the effects of discrimination involved in dysregulating the brain-gut microbiome (BGM) system. MethodsA total of 154 participants underwent brain magnetic resonance imaging to measure functional connectivity. Fecal samples were obtained for 16S ribosomal RNA profiling and fecal metabolites and serum for inflammatory markers, along with questionnaires. The Everyday Discrimination Scale was administered to measure chronic and routine experiences of unfair treatment. A sparse partial least squares-discriminant analysis was conducted to predict BGM alterations as a function of discrimination, controlling for sex, age, body mass index, and diet. Associations between discrimination-related BGM alterations and psychological variables were assessed using a tripartite analysis. ResultsDiscrimination was associated with anxiety, depression, and visceral sensitivity. Discrimination was associated with alterations of brain networks related to emotion, cognition and self-perception, and structural and functional changes in the gut microbiome. BGM discrimination-related associations varied by race/ethnicity. Among Black and Hispanic individuals, discrimination led to brain network changes consistent with psychological coping and increased systemic inflammation. For White individuals, discrimination was related to anxiety but not inflammation, while for Asian individuals, the patterns suggest possible somatization and behavioral (e.g., dietary) responses to discrimination. ConclusionsDiscrimination is attributed to changes in the BGM system more skewed toward inflammation, threat response, emotional arousal, and psychological symptoms. By integrating diverse lines of research, our results demonstrate evidence that may explain how discrimination contributes to health inequalities.

Dynamic Resting-State Functional Connectivity in Migraineurs

Functional magnetic resonance imaging (fMRI) is widely used to detect changes in the resting-state brain networks of migraine patients. Functional connectivity fMRI analysis examines the functional organization of the brain based on temporal correlations of blood oxygen level-dependent signal changes in different brain regions. Most previous resting-state fMRI studies have assumed that functional connectivity between brain regions remains relatively stable over time. However, it is now known that the brain is a complex system that undergoes time-dependent dynamics. Therefore, functional connectivity may change over time. In recent years, resting-state fMRI analysis has evolved from the detection of static coupling to the study of dynamic connectivity. However, studies of dynamic functional connectivity in migraine patients are limited. Related studies have shown that dynamic functional connectivity analysis reveals significant changes in connectivity and abnormal networks not found in static functional connectivity analysis.

Examining a window of vulnerability for affective symptoms in the mid-luteal phase of the menstrual cycle

Particular phases of the menstrual cycle may exacerbate affective symptoms for females with a diagnosed mental health disorder. However, there are mixed findings regarding whether affective symptoms change across the menstrual cycle in females without a clinical diagnosis. The window of vulnerability model proposes that natural increases in ovarian hormones in the mid-luteal phase of the menstrual cycle lead to systematic changes in brain networks associated with affective processing. Consequently, the model posits that females may experience stress more intensely and remember negative events more readily in the mid-luteal phase, increasing their risk for higher affective symptoms. Using a 35-day longitudinal study design, we tested the window of vulnerability model in a non-clinical sample. We tracked naturally cycling females’ daily stress and three types of affective symptoms: anxious apprehension, anxious arousal, and anhedonic depression. Using multilevel modeling, we simultaneously modeled within- and between-person associations among stress and menstrual phase for each affective symptom. We found increased anhedonic depression in the mid-luteal phase but not anxious apprehension or anxious arousal. Moreover, we detected a positive association between within- and between-person stress and anxious apprehension and anhedonic depression, but not anxious arousal. These associations were not stronger in the mid-luteal phase. Overall, we provide weak evidence for a window of vulnerability for affective symptoms in the mid-luteal phase of the menstrual cycle. Our findings suggest that stress is a better predictor of fluctuations in affective symptoms than the menstrual cycle. Moreover, our findings highlight the importance of measuring multiple negative affective symptoms because they may be differentially related to stress and the menstrual cycle.

Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence.

Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.

Detection of mild cognitive impairment in type 2 diabetes mellitus based on machine learning using privileged information

Type 2 diabetes mellitus (T2DM) patients may develop into mild cognitive impairment (MCI) or even dementia. However, there is lack of reliable machine learning model for detection MCI in T2DM patients based on machine learning method. In addition, the brain network changes associated with MCI have not been studied. The aim of this study is to develop a machine learning based algorithm to help detect MCI in T2DM. There are 164 participants were included in this study. They were divided into T2DM-MCI (n = 56), T2DM-nonMCI (n = 49), and normal controls (n = 59) according to the neuropsychological evaluation. Functional connectivity of each participant was constructed based on resting-state magnetic resonance imaging (rs-fMRI). Feature selection was used to reduce the feature dimension. Then the selected features were set into the cascaded multi-column random vector functional link network (RVFL) classifier model using privileged information. Finally, the optimal model was trained and the classification performance was obtained using the testing data. The results show that the proposed algorithm has outstanding performance compared with classic methods. The classification accuracy of 73.18 % (T2DM-MCI vs NC) and 79.42 % (T2DM-MCI vs T2DM-nonMCI) were achieved. The functional connectivity related to T2DM-MCI mainly distribute in the frontal lobe, temporal lobe, and central region (motor cortex), which could be used as neuroimaging biomarkers to recognize MCI in T2DM patients. This study provides a machine learning model for diagnosis of MCI in T2DM patients and has potential clinical significance for timely intervention and treatment to delay the development of MCI.