Amnestic Mild Cognitive Impairment Is Associated With Frequency-Specific Brain Network Alterations in Temporal Poles

Brain network topological changes in inflammatory bowel disease: an exploratory study

Although the aetio-pathogenesis of inflammatory bowel diseases (IBD) is not entirely clear, the interaction between genetic and adverse environmental factors may induce an intestinal dysbiosis, resulting in chronic inflammation having effects on the large-scale brain network. Here, we hypothesized inflammation-related changes in brain topology of IBD patients, regardless of the clinical form [ulcerative colitis (UC) or Crohn's disease (CD)]. To test this hypothesis, we analysed source-reconstructed magnetoencephalography (MEG) signals in 25 IBD patients (15 males, 10 females; mean age ± SD, 42.28 ± 13.15; mean education ± SD, 14.36 ± 3.58) and 28 healthy controls (HC) (16 males, 12 females; mean age ± SD, 45.18 ± 12.26; mean education ± SD, 16.25 ± 2.59), evaluating the brain topology. The betweenness centrality (BC) of the left hippocampus was higher in patients as compared with controls, in the gamma frequency band. It indicates how much a brain region is involved in the flow of information through the brain network. Furthermore, the comparison among UC, CD and HC showed statistically significant differences between UC and HC and between CD and HC, but not between the two clinical forms. Our results demonstrated that these topological changes were not dependent on the specific clinical form, but due to the inflammatory process itself. Broader future studies involving panels of inflammatory factors and metabolomic analyses on biological samples could help to monitor the brain involvement in IBD and to clarify the clinical impact.

Topological changes of fast large-scale brain dynamics in mild cognitive impairment predict early memory impairment: a resting-state, source reconstructed, magnetoencephalography study

Functional connectivity has been used as a framework to investigate widespread brain interactions underlying cognitive deficits in mild cognitive impairment (MCI). However, many functional connectivity metrics focus on the average of the periodic activities, disregarding the aperiodic bursts of activity (i.e., the neuronal avalanches) characterizing the large-scale dynamic activities of the brain. Here, we apply the recently described avalanche transition matrix framework to source-reconstructed magnetoencephalography signals in a cohort of 32 MCI patients and 32 healthy controls to describe the spatio-temporal features of neuronal avalanches and explore their topological properties. Our results showed that MCI patients showed a more centralized network (as assessed by higher values of the degree divergence and leaf fraction) as compared to healthy controls. Furthermore, we found that the degree divergence (in the theta band) was predictive of hippocampal memory impairment. These findings highlight the role of the changes of aperiodic bursts in clinical conditions and may contribute to a more thorough phenotypical assessment of patients.

Neurophysiological alterations in mice and humans carrying mutations in APP and PSEN1 genes

BACKGROUND

Studies in animal models of Alzheimer's disease (AD) have provided valuable insights into the molecular and cellular processes underlying neuronal network dysfunction. Whether and how AD-related neurophysiological alterations translate between mice and humans remains however uncertain.

METHODS

We characterized neurophysiological alterations in mice and humans carrying AD mutations in the APP and/or PSEN1 genes, focusing on early pre-symptomatic changes. Longitudinal local field potential recordings were performed in APP/PS1 mice and cross-sectional magnetoencephalography recordings in human APP and/or PSEN1 mutation carriers. All recordings were acquired in the left frontal cortex, parietal cortex, and hippocampus. Spectral power and functional connectivity were analyzed and compared with wildtype control mice and healthy age-matched human subjects.

RESULTS

APP/PS1 mice showed increased absolute power, especially at higher frequencies (beta and gamma) and predominantly between 3 and 6 moa. Relative power showed an overall shift from lower to higher frequencies over almost the entire recording period and across all three brain regions. Human mutation carriers, on the other hand, did not show changes in power except for an increase in relative theta power in the hippocampus. Mouse parietal cortex and hippocampal power spectra showed a characteristic peak at around 8 Hz which was not significantly altered in transgenic mice. Human power spectra showed a characteristic peak at around 9 Hz, the frequency of which was significantly reduced in mutation carriers. Significant alterations in functional connectivity were detected in theta, alpha, beta, and gamma frequency bands, but the exact frequency range and direction of change differed for APP/PS1 mice and human mutation carriers.

CONCLUSIONS

Both mice and humans carrying APP and/or PSEN1 mutations show abnormal neurophysiological activity, but several measures do not translate one-to-one between species. Alterations in absolute and relative power in mice should be interpreted with care and may be due to overexpression of amyloid in combination with the absence of tau pathology and cholinergic degeneration. Future studies should explore whether changes in brain activity in other AD mouse models, for instance, those also including tau pathology, provide better translation to the human AD continuum.

Topologically convergent and divergent large scale complex networks among Alzheimer's disease spectrum patients: A systematic review

Alzheimer's disease (AD) is associated with disruption at the level of a large-scale complex network. To explore the underlying mechanisms in the progression of AD, graph theory was used to quantitatively analyze the topological properties of structural and functional connections. Although an increasing number of studies have shown altered global and nodal network properties, little is known about the topologically convergent and divergent patterns between structural and functional networks among AD-spectrum patients. In this review, we summarized the topological patterns of the large-scale complex networks using multimodal neuroimaging graph theory analysis in AD spectrum patients. Convergent deficits in the connectivity characteristics were primarily in the default mode network (DMN) itself both in the structural and functional networks, while a divergent changes in the neighboring regions of the DMN were also observed between the patient groups. Together, the application of graph theory to large-scale complex brain networks provides quantitative insights into topological principles of brain network organization, which may lead to increasing attention in identifying the underlying neuroimaging pathological changes and predicting the progression of AD.

Comparison of classification algorithms for predicting autistic spectrum disorder using WEKA modeler

BACKGROUND

In healthcare area, big data, if integrated with machine learning, enables health practitioners to predict the result of a disorder or disease more accurately. In Autistic Spectrum Disorder (ASD), it is important to screen the patients to enable them to undergo proper treatments as early as possible. However, difficulties may arise in predicting ASD occurrences accurately, mainly caused by human errors. Data mining, if embedded into health screening practice, can help to overcome the difficulties. This study attempts to evaluate the performance of six best classifiers, taken from existing works, at analysing ASD screening training dataset.

RESULT

We tested Naive Bayes, Logistic Regression, KNN, J48, Random Forest, SVM, and Deep Neural Network algorithms to ASD screening dataset and compared the classifiers' based on significant parameters; sensitivity, specificity, accuracy, receiver operating characteristic, area under the curve, and runtime, in predicting ASD occurrences. We also found that most of previous studies focused on classifying health-related dataset while ignoring the missing values which may contribute to significant impacts to the classification result which in turn may impact the life of the patients. Thus, we addressed the missing values by implementing imputation method where they are replaced with the mean of the available records found in the dataset.

CONCLUSION

We found that J48 produced promising results as compared to other classifiers when tested in both circumstances, with and without missing values. Our findings also suggested that SVM does not necessarily perform well for small and simple datasets. The outcome is hoped to assist health practitioners in making accurate diagnosis of ASD occurrences in patients.

Distinct Effects of the Apolipoprotein E ε4 Genotype on Associations Between Delayed Recall Performance and Resting-State Electroencephalography Theta Power in Elderly People Without Dementia

Background

Abnormal electroencephalography (EEG) activity has been demonstrated in mild cognitive impairment (MCI), and theta rhythm might be inversely related to memory. The apolipoprotein E (ApoE) epsilon 4 (ε4) allele, as a genetic vulnerability factor for pathologic and normal age-related cognitive decline, may influence different patterns of cognitive dysfunction. Therefore, the present study primarily aimed to verify the role of resting theta rhythm in delayed recall deficits, and further explore the effects of the ApoE genotype on the associations between the resting theta power and delayed recall performance in the elderly individuals without dementia.

Methods

A total of 47 individuals without dementia, including 23 MCI and 24 healthy subjects (HCs), participated in the study. All subjects were administered the Hopkins Verbal Learning Test–Revised (HVLT-R) to measure delayed recall performance. Power spectra based on resting-state EEG data were used to examine brain oscillations. Linear regression was used to examine the relationships between EEG power and delayed recall performance in each subgroup.

Results

The increased theta power in the bilateral central and temporal areas (P_s = 0.02–0.044, uncorrected) was found in the patients with MCI, and were negatively correlated with delayed recall performance (r_s = −0.358 to −0.306, P_s = 0.014–0.036, FDR corrected) in the elderly individuals without dementia. The worse delayed recall performance was associated with higher theta power in the left central and temporal areas, especially in ApoE ε4 non-carriers and not in carriers (r_s = −0.404 to −0.369, P_s = 0.02–0.035, uncorrected).

Conclusion

Our study suggests that theta disturbances might contribute to delayed recall memory decline. The ApoE genotype may have distinct effects on EEG-based neural correlates of episodic memory performance.

Minimum spanning tree analysis of brain networks: A systematic review of network size effects, sensitivity for neuropsychiatric pathology and disorder specificity

Brain network characteristics’ potential to serve as a neurological and psychiatric pathology biomarker has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. It is yet unknown whether this approach leads to more consistent findings across studies and converging outcomes of either disease-specific biomarkers or transdiagnostic effects. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies (N = 43) to study consistency of MST metrics between different network sizes and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. Analysis of data from control groups (12 studies) showed that MST leaf fraction but not diameter decreased with increasing network size. Studies showed a broad range in metric values, suggesting that specific processing pipelines affect MST topology. Contradicting findings remain in the inconclusive literature of MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders across pathologies, and is associated with symptom severity and disease progression; (2) neurophysiological studies in epilepsy show frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology in alpha band is found across disorders associated with attention impairments.

The potential of brain network characteristics to serve as biomarker of neurological and psychiatric pathology has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. MST leaf fraction but not diameter decreased with increasing network size. Contradicting findings remain in the literature on MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders; (2) in epilepsy there are frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology is found across disorders associated with attention impairments.

A night of sleep deprivation alters brain connectivity and affects specific executive functions

Sleep is a fundamental physiological process necessary for efficient cognitive functioning especially in relation to memory consolidation and executive functions, such as attentional and switching abilities. The lack of sleep strongly alters the connectivity of some resting-state networks, such as default mode network and attentional network. In this study, by means of magnetoencephalography (MEG) and specific cognitive tasks, we investigated how brain topology and cognitive functioning are affected by 24 h of sleep deprivation (SD). Thirty-two young men underwent resting-state MEG recording and evaluated in letter cancellation task (LCT) and task switching (TS) before and after SD. Results showed a worsening in the accuracy and speed of execution in the LCT and a reduction of reaction times in the TS, evidencing thus a worsening of attentional but not of switching abilities. Moreover, we observed that 24 h of SD induced large-scale rearrangements in the functional network. These findings evidence that 24 h of SD is able to alter brain connectivity and selectively affects cognitive domains which are under the control of different brain networks.

Altered functional connectivity density in mild cognitive impairment with moxibustion treatment: A resting-state fMRI study

PURPOSE

Mild cognitive impairment (MCI) is a general neurodegenerative disease. Moxibustion has been shown to have remarkable effect on cognitive improvement, however, less is known about the effect of moxibustion on MCI and its underlying neural mechanism. This study aimed to investigate the ameliorative brain network in MCI after treatments of acupoint-related moxibustion.

METHODS

Resting-state functional MRI were derived from 47 MCI patients and 30 healthy controls (HCs). Patients were randomized as Tiaoshen YiZhi (TSYZ, n = 27) and sham (SHAM, n = 20) acupoint moxibustion groups. Functional connectivity density (FCD) method and repeated-measures two-way analysis of variance (ANOVA) were performed to ascertain the interaction effects between groups (TSYZ and SHAM) and time (baseline and post-treatment). Abnormal FCD was examined between baseline and post-treatment in TSYZ and SHAM groups, respectively.

RESULTS

Compared with HCs, MCI showed altered FCD in the middle frontal cortex (MFC), inferior frontal cortex, temporal pole, thalamus and middle cingulate cortex. After moxibustion treatment in MCI, 1) a significant time-by-groups interaction was observed in the medial prefrontal cortex (mPFC); 2) abnormal long-range FCD (lrFCD) in the mPFC and MFC were modulated in TSYZ group; 3) significantly improved clinical symptoms; 4) changed lrFCD in the MFC was significantly negatively correlated with the increased Montreal Cognitive Assessment scores in TSYZ group.

CONCLUSIONS

These imaging findings suggest that treatments of acupoint-related moxibustion could improve lrFCD in certain regions related to self-related cognitive and decision making. Our study might promote understanding of MCI neural mechanisms and expand the clinical application of moxibustion in MCI.

Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals

The oscillatory patterns of electroencephalography (EEG), during resting states, are informative and helpful in understanding the functional states of brain network and their contribution to behavioral performances. The aim of this study is to characterize the functional brain network alterations in patients with amnestic mild cognitive impairment (aMCI). To this end, rsEEG signals were recorded before and after a cognitive task. Functional connectivity metrics were calculated using debiased weighted phase lag index (DWPLI). Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. Subsequently, the network and connectivity values together with Mini-Mental State Examination cognitive test were used as features to classify the participants. Results showed that: (1) across the pre-task condition, in the theta band, the aMCI group had a significantly lower global mean DWPLI than the control group; the functional connectivity patterns were different in the left hemisphere between two groups; the aMCI group showed significantly higher average clustering coefficient and the remarkably lower global efficiency than the control. (2) Analysis of graph measures under post-task resting state, unveiled that for the percentage change of post-task vs. pre-task in beta EEG, a significant increase in tree hierarchy was observed in aMCI group (2.41%) than in normal control (-3.89%); (3) Furthermore, the classification analysis of combined measures of functional connectivity, brain topology, and MMSE test showed improved accuracy compared to the single method, for which the connectivity patterns and graph metrics were used as separate inputs. The classification accuracy obtained for the case of post-task resting state was 87.2%, while the one achieved under pre-task resting state was found to be 77.7%. Therefore, the functional network alterations in aMCI patients were more prominent during the post-task resting state. This study suggests that the disintegration observed in MCI functional network during the resting states, preceding and following a task, might be possible biomarkers of cognitive dysfunction in aMCI patients.

Detection of Mild Cognitive Impairment with MEG Functional Connectivity Using Wavelet-Based Neuromarkers

Studies on developing effective neuromarkers based on magnetoencephalographic (MEG) signals have been drawing increasing attention in the neuroscience community. This study explores the idea of using source-based magnitude-squared spectral coherence as a spatial indicator for effective regions of interest (ROIs) localization, subsequently discriminating the participants with mild cognitive impairment (MCI) from a group of age-matched healthy control (HC) elderly participants. We found that the cortical regions could be divided into two distinctive groups based on their coherence indices. Compared to HC, some ROIs showed increased connectivity (hyper-connected ROIs) for MCI participants, whereas the remaining ROIs demonstrated reduced connectivity (hypo-connected ROIs). Based on these findings, a series of wavelet-based source-level neuromarkers for MCI detection are proposed and explored, with respect to the two distinctive ROI groups. It was found that the neuromarkers extracted from the hyper-connected ROIs performed significantly better for MCI detection than those from the hypo-connected ROIs. The neuromarkers were classified using support vector machine (SVM) and k-NN classifiers and evaluated through Monte Carlo cross-validation. An average recognition rate of 93.83% was obtained using source-reconstructed signals from the hyper-connected ROI group. To better conform to clinical practice settings, a leave-one-out cross-validation (LOOCV) approach was also employed to ensure that the data for testing was from a participant that the classifier has never seen. Using LOOCV, we found the best average classification accuracy was reduced to 83.80% using the same set of neuromarkers obtained from the ROI group with functional hyper-connections. This performance surpassed the results reported using wavelet-based features by approximately 15%. Overall, our work suggests that (1) certain ROIs are particularly effective for MCI detection, especially when multi-resolution wavelet biomarkers are employed for such diagnosis; (2) there exists a significant performance difference in system evaluation between research-based experimental design and clinically accepted evaluation standards.

Brain networks and cognitive impairment in Parkinson's disease

Aim The aim of the present study is to investigate the relations between both functional connectivity and brain networks with cognitive decline, in patients with Parkinson's disease (PD). Introduction PD phenotype is not limited to motor impairment but, rather, a wide range of non-motor disturbances can occur, cognitive impairment being one of the commonest. However, how the large-scale organization of brain activity differs in cognitively impaired patients, as opposed to cognitively preserved ones, remains poorly understood. Methods Starting from source-reconstructed resting-state magnetoencephalography data, we applied the PLM to estimate functional connectivity, globally and between brain areas, in PD patients with and without cognitive impairment (respectively PD-CI and PD-NC), as compared to healthy subjects (HS). Furthermore, using graph analysis, we characterized the alterations in brain network topology and related these, as well as the functional connectivity, to cognitive performance. Results We found reduced global and nodal PLM in several temporal (fusiform gyrus, Heschl's gyrus and inferior temporal gyrus), parietal (postcentral gyrus), and occipital (lingual gyrus) areas within the left hemisphere, in the gamma band, in PD-CI patients, as compared to PD-NC and HS. With regard to the global topological features, PD-CI patients, as compared to HS and PD-NC patients, showed differences in multi frequencies bands (delta, alpha, gamma) in the Leaf fraction, Tree hierarchy (both higher in PD-CI) and Diameter (lower in PD-CI). Finally, we found statistically significant correlations between the MoCA test and both the Diameter in delta band and the Tree Hierarchy in the alpha band. Conclusion Our work points to specific large-scale rearrangements that occur selectively in cognitively compromised PD patients and correlated to cognitive impairment.

Functional brain network topology across the menstrual cycle is estradiol dependent and correlates with individual well-being

The menstrual cycle (MC) is a sex hormone‐related phenomenon that repeats itself cyclically during the woman's reproductive life. In this explorative study, we hypothesized that coordinated variations of multiple sex hormones may affect the large‐scale organization of the brain functional network and that, in turn, such changes might have psychological correlates, even in the absence of overt clinical signs of anxiety and/or depression. To test our hypothesis, we investigated longitudinally, across the MC, the relationship between the sex hormones and both brain network and psychological changes. We enrolled 24 naturally cycling women and, at the early‐follicular, peri‐ovulatory, and mid‐luteal phases of the MC, we performed: (a) sex hormone dosage, (b) magnetoencephalography recording to study the brain network topology, and (c) psychological questionnaires to quantify anxiety, depression, self‐esteem, and well‐being. We showed that during the peri‐ovulatory phase, in the alpha band, the leaf fraction and the tree hierarchy of the brain network were reduced, while the betweenness centrality (BC) of the right posterior cingulate gyrus (rPCG) was increased. Furthermore, the increase in BC was predicted by estradiol levels. Moreover, during the luteal phase, the variation of estradiol correlated positively with the variations of both the topological change and environmental mastery dimension of the well‐being test, which, in turn, was related to the increase in the BC of rPCG. Our results highlight the effects of sex hormones on the large‐scale brain network organization as well as on their possible relationship with the psychological state across the MC. Moreover, the fact that physiological changes in the brain topology occur throughout the MC has widespread implications for neuroimaging studies.

Impact of Transcranial Direct Current Stimulation on Cognitive Function, Brain Functional Segregation, and Integration in Patients with Mild Cognitive Impairment According to Amyloid-Beta Deposition and APOE ε4-Allele: A Pilot Study

Anodal transcranial direct current stimulation (anodal-tDCS) is known to improve cognition and normalize abnormal network configuration during resting-state functional magnetic resonance imaging (fMRI) in patients with mild cognitive impairment (MCI). We aimed to evaluate the impact of sequential anodal-tDCS on cognitive functions, functional segregation, and integration parameters in patients with MCI, according to high-risk factors for Alzheimer’s disease (AD): amyloid-beta (Aβ) deposition and APOE ε4-allele status. In 32 patients with MCI ([¹⁸ F] flutemetamol-: n = 10, [¹⁸ F] flutemetamol+: n = 22; APOE ε4-: n = 13, APOE ε4+: n = 19), we delivered anodal-tDCS (2 mA/day, five times/week, for 2 weeks) over the left dorsolateral prefrontal cortex and assessed the neuropsychological test battery and resting-state fMRI measurements before and after 2 weeks stimulation. We observed a non-significant impact of an anodal-tDCS on changes in neuropsychological battery scores between MCI patients with and without high-risk factors of AD, Aβ retention and APOE ε4-allele. However, there was a significant difference in brain functional segregation and integration parameters between MCI patients with and without AD high-risk factors. We also found a significant effect of tDCS-by-APOE ε4-allele interaction on changes in the functional segregation parameter of the temporal pole. In addition, baseline Aβ deposition significantly associated negatively with change in global functional integrity of hippocampal formation. Anodal-tDCS might help to enhance restorative and compensatory intrinsic functional changes in MCI patients, modulated by the presence of Aβ retention and the APOE ε4-allele.

Clinical connectome fingerprints of cognitive decline

Brain connectome fingerprinting is rapidly rising as a novel influential field in brain network analysis. Yet, it is still unclear whether connectivity fingerprints could be effectively used for mapping and predicting disease progression from human brain data. We hypothesize that dysregulation of brain activity in disease would reflect in worse subject identification. We propose a novel framework, Clinical Connectome Fingerprinting, to detect individual connectome features from clinical populations. We show that "clinical fingerprints" can map individual variations between elderly healthy subjects and patients with mild cognitive impairment in functional connectomes extracted from magnetoencephalography data. We find that identifiability is reduced in patients as compared to controls, and show that these connectivity features are predictive of the individual Mini-Mental State Examination (MMSE) score in patients. We hope that the proposed methodology can help in bridging the gap between connectivity features and biomarkers of brain dysfunction in large-scale brain networks.

Neuronal Avalanches to Study the Coordination of Large-Scale Brain Activity: Application to Rett Syndrome

Many complex systems, such as the brain, display large-scale coordinated interactions that create ordered patterns. Classically, such patterns have been studied using the framework of criticality, i.e., at a transition point between two qualitatively distinct patterns. This kind of system is generally characterized by a scale-invariant organization, in space and time, optimally described by a power-law distribution whose slope is quantified by an exponent α. The dynamics of these systems is characterized by alternating periods of activations, called avalanches, with quiescent periods. To maximize its efficiency, the system must find a trade-off between its stability and ease of propagation of activation, which is achieved by a branching process. It is quantified by a branching parameter σ defined as the average ratio between the number of activations in consecutive time bins. The brain is itself a complex system and its activity can be described as a series of neuronal avalanches. It is known that critical aspects of brain dynamics are modeled with a branching parameter σ = , and the neuronal avalanches distribution fits well with a power law distribution exponent α = -3/2. The aim of our work was to study a self-organized criticality system in which there was a change in neuronal circuits due to genetic causes. To this end, we have compared the characteristics of neuronal avalanches in a group of 10 patients affected by Rett syndrome, during an open-eye resting-state condition estimated using magnetoencephalography, with respect to 10 healthy subjects. The analysis was performed both in broadband and in the five canonical frequency bands. We found, for both groups, a branching parameter close to 1. In this critical condition, Rett patients show a lower distribution parameter α in the delta and broadband. These results suggest that the large-scale coordination of activity occurs to a lesser extent in RTT patients.

Wide temporal horns are associated with cognitive dysfunction, as well as impaired gait and incontinence

The association between morphology of the brain and symptoms of suspected idiopathic normal pressure hydrocephalus (iNPH) is largely unknown. We investigated how ventricular expansion (width of the temporal horns [TH], callosal angle [CA], and Evans' index [EI]) related to symptom severity in suspected iNPH. Participants (n = 168; 74.9 years ± SD 6.7; 55% females) from the general population underwent neurological examination, computed tomography, and neuropsychological testing. Multiple linear regression analysis revealed that wide TH was independently associated with all examined iNPH symptoms (p < 0.01) except for fine-motor performance, whereas a narrow CA only was associated to specific motor and cognitive functions (p < 0.05). TH and EI correlated significantly with incontinence (r_s 0.17 and r_s 0.16; p < 0.05). In conclusion, wide TH was significantly associated with most iNPH-symptoms. This finding potentially reflects the complex nature of the hippocampus, however further studies are needed to demonstrate functional connectivity.

Disrupted Patterns of Rich-Club and Diverse-Club Organizations in Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment

Background

Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) were considered to be a continuum of Alzheimer’s disease (AD) spectrum. The abnormal topological architecture and rich-club organization in the brain functional network can reveal the pathology of the AD spectrum. However, few studies have explored the disrupted patterns of diverse club organizations and the combination of rich- and diverse-club organizations in SCD and aMCI.

Methods

We collected resting-state functional magnetic resonance imaging data of 19 SCDs, 29 aMCIs, and 28 healthy controls (HCs) from the Alzheimer’s Disease Neuroimaging Initiative. Graph theory analysis was used to analyze the network metrics and rich- and diverse-club organizations simultaneously.

Results

Compared with HC, the aMCI group showed altered small-world and network efficiency, whereas the SCD group remained relatively stable. The aMCI group showed reduced rich-club connectivity compared with the HC. In addition, the aMCI group showed significantly increased feeder connectivity and decreased local connectivity of the diverse club compared with the SCD group. The overlapping nodes of the rich club and diverse club showed a significant difference in nodal efficiency and shortest path length (L_p) between groups. Notably, the L_p values of overlapping nodes in the SCD and aMCI groups were significantly associated with episodic memory.

Conclusion

The present study demonstrates that the network properties of SCD and aMCI have varying degrees of damage. The combination of the rich club and the diverse club can provide a novel insight into the pathological mechanism of the AD spectrum. The altered patterns in overlapping nodes might be potential biomarkers in the diagnosis of the AD spectrum.

In Amyotrophic Lateral Sclerosis Blood Cytokines Are Altered, but Do Not Correlate with Changes in Brain Topology

Aim: The present study aims at investigating the possible correlation between peripheral markers of inflammation and brain networks. Introduction: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease dominated by progressive motor impairment. Among the complex mechanisms contributing to the pathogenesis of the disease, neuroinflammation, which is associated with altered circulating cytokine levels, is suggested to play a prominent role. Methods: Based on magnetoencephalography data, we estimated topological properties of the brain networks in ALS patients and healthy controls. Subsequently, the blood levels of a subset of cytokines were assayed. Finally, we modeled the brain topological features in the function of the cytokine levels. Results: Significant differences were found in the levels of the cytokines interleukin (IL)-4, IL-1β, and interferon-gamma (IFN-γ) between patients and controls. In particular, IL-4 and IL-1β levels increased in ALS patients, while the IFN-γ level was higher in healthy controls. We also detected modifications in brain global topological parameters in terms of hyperconnectedness. Despite both blood cytokines and brain topology being altered in ALS patients, such changes do not appear to be in a direct relationship. Conclusion: Our results would be in line with the idea that topological changes relate to neurodegenerative processes. However, the absence of correlation between blood cytokines and topological parameters of brain networks does not preclude that inflammatory processes contribute to the alterations of the brain networks. Impact statement The progression of amyotrophic lateral sclerosis entails both neurodegenerative and inflammatory processes. Furthermore, disease progression induces global modifications of the brain networks, with advanced stages showing a more compact, hyperconnected network topology. The pathophysiological processes underlying topological changes are unknown. In this article, we hypothesized that the global inflammatory profile would relate to the topological alterations. Our results showed that this is not the case, as modeling the topological properties as a function of the inflammatory state did not yield good predictions. Hence, our results suggest that topological changes might directly relate to neurodegenerative processes instead.

Brain Network Integration in Patients with Migraine: A Magnetoencephalography Study

Migraine is a common disorder with high social and medical impact. Patients with migraine have a much higher chance of experiencing headache attacks compared with the general population. Recent neuroimaging studies have confirmed that pathophysiology in the brain is not only limited to the moment of the attack but is also present in between attacks, the interictal phase. In this study, we hypothesized that the topology of functional brain networks is also different in the interictal state, compared with people who are not affected by migraine. We also expected that the level of network disturbances scales with the number of years people have suffered from migraine. Functional connectivity between 78 cortical brain regions was estimated for source-level magnetoencephalography data by calculating the phase lag index, in five frequency bands (delta-beta), and compared between healthy controls (n = 24) and patients who had been suffering from migraine for longer than 6 years (n = 12) or shorter than 6 years (n = 12). Moreover, the topology of the functional networks was characterized using the minimum spanning tree. The migraine groups did not differ from each other in functional connectivity. However, the network topology was different compared with healthy controls. The results were frequency specific, and higher average nodal betweenness centrality was specifically evident in higher frequency bands in patients with longer disease duration, while an opposite trend was present for lower frequencies. This study shows that patients with migraine have a different network topology in the resting state compared with healthy controls, whereby specific brain areas have altered topological roles in a frequency-specific manner. Some alterations appear specifically in patients with long-term migraine, which might show the long-term effects of the disease.

Bexarotene treatment increases dendritic length in the nucleus accumbens without change in the locomotor activity and memory behaviors, in old mice

The aged brain has biochemical and morphological alterations in the dendrites of the pyramidal neurons of the limbic system, which consequently trigger motor and cognitive deficits. Bexarotene 4-[1-(3,5,5,8,8-pentamethyl-6,7-dihydronaphthalen-2-yl)ethenyl]benzoic acid is a selective agonist of X-retinoid receptors which acts by binding to the intracellular retinoic acid receptors (RAR). It decreases oxidative and inflammatory activity, in addition to the transport of lipids, mechanisms that together could have a neuroprotective effect. Our objective was to evaluate the effect of bexarotene on the motor and cognitive processes, as well as its influence on the dendritic morphology of neurons in the limbic system of elderly mice. Dendritic morphology was evaluated with the Golgi-Cox staining procedure followed by the Sholl analysis. Bexarotene was administered at different doses: 0.0; 0.5; 2.5 and 5.0 mg/kg for 60 days in 18-month-old mice. After the treatment, locomotor activity in a novel environment and spatial memory in the water labyrinth were evaluated. Mice treated with bexarotene did not show significant changes in their behavior. Moreover, bexarotene-treated mice only showed a significant increase in the density of the dendritic spines and the dendritic length in the nucleus accumbens (NAcc) neurons. In conclusion, the administration of bexarotene improves the plasticity of the NAcc of aged mice, and therefore could be a pharmacological alternative to prevent or delay neuroplasticity disruptions in brain aging.