Brain Functional Connectivity Analysis Research Articles

Enhancement of astrocytic gap junctions Connexin43 coupling can improve long-term isoflurane anesthesia-mediated brain network abnormalities and cognitive impairment.

Astrocytes are connected by gap junctions Connexin43 (GJs-Cx43) forming an extensive intercellular network and maintain brain homeostasis. Perioperative neurocognitive disorder (PND) occurs frequently after anesthesia/surgery and worsens patient outcome, but the neural circuit mechanisms remain unclear. This study aimed to determine the effects of the GJs-Cx43-mediated astrocytic network on PND and ascertain the underlying neural circuit mechanism. Male C57BL/6 mice were treated with long-term isoflurane exposure to construct a mouse model of PND. We also exposed primary mouse astrocytes to long-term isoflurane exposure to simulate the conditions of in vivo cognitive dysfunction. Behavioral tests were performed using the Y-maze and fear conditioning (FC) tests. Manganese-enhanced magnetic resonance imaging (MEMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) were used to investigate brain activity and functional connectivity. Western blot and flow cytometry analysis were used to assess protein expression. Reconfiguring the astrocytic network by increasing GJs-Cx43 expression can modulate 22 subregions affected by PND in three ways: reversed activation, reversed inhibition, and intensified activation. The brain functional connectivity analysis further suggests that PND is a brain network disorder that includes sleep-wake rhythm-related brain regions, contextual and fear memory-related subregions, the hippocampal-amygdala circuit, the septo-hippocampal circuit, and the entorhinal-hippocampal circuit. Notably, remodeling the astrocytic network by upregulation of GJs-Cx43 can partially reverse the abnormalities in the above circuits. Pathophysiological degeneration in hippocampus is one of the primary hallmarks of PND pathology, and long-term isoflurane anesthesia contributes to oxidative stress and neuroinflammation in the hippocampus. However, promoting the formation of GJs-Cx43 ameliorated cognitive dysfunction induced by long-term isoflurane anesthesia through the attenuation of oxidative stress in hippocampus. Enhancing GJs-Cx43 coupling can improve brain network abnormalities and cognitive impairment induced by long-term isoflurane anesthesia, its mechanisms might be associated with the regulation of oxidative stress and neuroinflammation.

Brain functional connectivity analysis in patients with relapsing-remitting multiple sclerosis: A graph theory approach of EEG resting state.

Multiple sclerosis (MS) is an autoimmune disease related to the central nervous system (CNS). This study aims to investigate the effects of MS on the brain's functional connectivity network using the electroencephalogram (EEG) resting-state signals and graph theory approach. Resting-state eyes-closed EEG signals were recorded from 20 patients with relapsing-remitting MS (RRMS) and 18 healthy cases. In this study, the prime objective is to calculate the connectivity between EEG channels to assess the differences in brain functional network global features. The results demonstrated lower cortical activity in the alpha frequency bands and higher activity for the gamma frequency bands in patients with RRMS compared to the healthy group. In this study, graph metric calculations revealed a significant difference in the diameter of the functional brain network based on the directed transfer function (DTF) measure between the two groups, indicating a higher diameter in RRMS cases for the alpha frequency band. A higher diameter for the functional brain network in MS cases can result from anatomical damage. In addition, considerable differences between the networks' global efficiency and transitivity based on the imaginary part of the coherence (iCoh) measure were observed, indicating higher global efficiency and transitivity in the delta, theta, and beta frequency bands for RRMS cases, which can be related to the compensatory functional reaction from the brain. This study indicated that in RRMS cases, some of the global characteristics of the brain's functional network, such as diameter and global efficiency, change and can be illustrated even in the resting-state condition when the brain is not under cognitive load.

Knowing what you feel: Inferior frontal gyrus-based structural and functional neural patterns underpinning adaptive body awareness

BackgroundHeightened body awareness (BA) is conducive for increasing understanding of bodily state and improves individuals' health and well-being. Although there has been cumulative research concentrating on the self-perceived tendency to focus on negatively valenced interoceptive sensations, the specific structural and functional neural patterns underlying BA and their role in the relationship between BA and individual well-being remain unclear. MethodsVoxel-based morphometry and whole brain functional connectivity analyses were conducted to examine the structural and functional neural patterns, respectively, in 686 healthy subjects. BA and subjective well-being were assessed using questionnaires. ResultsBA was inversely related to gray matter volume of the right inferior frontal gyrus, opercular part (IFGoperc). Higher BA was correlated with enhanced IFGoperc-precuneus and IFGoperc-anterior supramarginal gyrus connectivities, and with decreased IFGoperc-lateral occipital cortex and IFGoperc-medial frontal cortex connectivities. The inferior frontal gyrus, triangular part (in the fronto-parietal task control network) acted as the hub that linked the sensory/somatomotor network, the default mode network, and the dorsal and ventral attention network. The IFGoperc-precuneus connectivity moderated the association between BA and subjective well-being. LimitationsWe were unable to rank all the networks by their relative importance, because the absolute weighted value in each module was not calculated. ConclusionOur findings demonstrated that BA was reflected by specific neural patterns mainly involved in cognitive-affective control, attentional and self-referential processing, as well as multisensory integration, which could offer some references for current therapies (e.g., mindfulness, yoga training) that are dedicated to solving health problems and improving individual well-being.

EEG-FCV: An EEG-Based Functional Connectivity Visualization Framework for Cognitive State Evaluation

Electroencephalogram (EEG)-based tools for brain functional connectivity (FC) analysis and visualization play an important role in evaluating brain cognitive function. However, existing similar FC analysis tools are not only visualized in 2 dimensions (2D) but also are highly prone to cause visual clutter and unable to dynamically reflect brain connectivity changes over time. Therefore, we design and implement an EEG-based FC visualization framework in this study, named EEG-FCV, for brain cognitive state evaluation. EEG-FCV is composed of three parts: the Data Processing module, Connectivity Analysis module, and Visualization module. Specially, FC is visualized in 3 dimensions (3D) by introducing three existing metrics: Pearson Correlation Coefficient (PCC), Coherence, and PLV. Furthermore, a novel metric named Comprehensive is proposed to solve the problem of visual clutter. EEG-FCV can also visualize dynamically brain FC changes over time. Experimental results on two available datasets show that EEG-FCV has not only results consistent with existing related studies on brain FC but also can reflect dynamically brain FC changes over time. We believe EEG-FCV could prompt further progress in brain cognitive function evaluation.

Recognizing the individualized sensorimotor loop of stroke patients during BMI-supported rehabilitation training based on brain functional connectivity analysis

BackgroundElectroencephalogram (EEG) based brain-machine interaction training can facilitate rehabilitation by closing the sensorimotor loop. However, it remains unclear how to evaluate whether the loop is closed, especially for stroke patients whose brain regions of motor control and sensorimotor feedback could be altered. Our hypothesis is that motor recovery depends on whether sensorimotor loop is established poststroke. This study aims to explore how to evaluate the establishment of sensorimotor loop based on the evolving neural reorganization patterns after stroke. New Method14 stroke patients participated in the experiment and EEG were recorded during three specific tasks: Movement Imagery (MI), Passive Movement (PM) and Movement Execution (ME). Activated brain regions correlated with movement intention expression and sensorimotor feedback were detected respectively during MI and PM. In ME, local-averaged Phase Lag Index (PLI) was analyzed to represent the functional connectivity between activated brain regions of MI and PM. ResultsIndividualized cortical activation was found both in MI and PM. The overlapping brain activation during PM and MI did not correlate with patient’s Fugl-Meyer Upper Extremity Motor Score (FMU) . However, we found that FMU of the group with higher local-averaged PLI was statistically higher than FMU of the group with lower local-averaged PLI compared with global-averaged PLI (p < 0.05). ConclusionsThe findings demonstrate functional connectivity between activated brain regions of motor control and sensorimotor feedback may imply if the individualized sensorimotor loop is established poststroke. The successful formation of the closed loop can indicate stroke patients’ motor recovery.

Brain Functional Connectivity Analysis via Graphical Deep Learning.

Graphical deep learning models provide a desirable way for brain functional connectivity analysis. However, the application of current graph deep learning models to brain network analysis is challenging due to the limited sample size and complex relationships between different brain regions. In this work, a graph convolutional network (GCN) based framework is proposed by exploiting the information from both region-to-region connectivities of the brain and subject-subject relationships. We first construct an affinity subject-subject graph followed by GCN analysis. A Laplacian regularization term is introduced in our model to tackle the overfitting problem. We apply and validate the proposed model to the Philadelphia Neurodevelopmental Cohort for the brain cognition study. Experimental analysis shows that our proposed framework outperforms other competing models in classifying groups with low and high Wide Range Achievement Test (WRAT) scores. Moreover, to examine each brain region's contribution to cognitive function, we use the occlusion sensitivity analysis method to identify cognition-related brain functional networks. The results are consistent with previous research yet yield new findings. Our study demonstrates that GCN incorporating prior knowledge about brain networks offers a powerful way to detect important brain networks and regions associated with cognitive functions.

Effect of Acupuncture on the Cognitive Control Network of Patients with Knee Osteoarthritis: Study Protocol for a Randomized Controlled Trial.

PurposeAbnormal central nervous system function is the key central pathological factor leading to chronic pain in patients with knee osteoarthritis (KOA). Acupuncture can effectively relieve the pain of KOA patients. However, the central nervous mechanism of acupuncture treating KOA is not fully understood. This trial will use functional magnetic resonance imaging (fMRI) analysis techniques to investigate the potential central nervous mechanism of acupuncture treatment of KOA.Materials and MethodsA total of 108 patients will be randomized (in a 1:1:1 ratio) into three groups, this trial will include 4-week treatment, patients in groups A and B will receive 20 acupuncture and sham acupuncture sessions, respectively, patients in group C will not receive any intervention, and all patients will receive fMRI scans before and after the intervention. The Western Ontario and McMaster Universities Osteoarthritis Index score (WOMAC) will be the primary clinical outcome. Then, we will explore the functional changes of the cognitive control network (CCN) in the brains of KOA patients through whole brain functional connectivity (FC) analysis and seed-based functional connectivity (sFC) analysis. Pearson correlation coefficient will be used to analyze the relationship between the improved value of the clinical correlation scale and the change of fMRI data.DiscussionThis trial will analyze the efficacy of verum acupuncture, sham acupuncture and the waiting-list for KOA and explore the activity of the CCN in three groups of patients by fMRI, so as to reveal the central nervous mechanisms of acupuncture in the treatment of KOA.Study RegistrationThis study is approved by the Ethics Committee of the First Affiliated Hospital of Henan University of Traditional Chinese Medicine (No: 2019HL-133-01) and registered in the Chinese Clinical Trial Registry, ChiCTR2000038554.

Is There a Difference in Brain Functional Connectivity between Chinese Coal Mine Workers Who Have Engaged in Unsafe Behavior and Those Who Have Not?

(1) Background: As a world-recognized high-risk occupation, coal mine workers need various cognitive functions to process the surrounding information to cope with a large number of perceived hazards or risks. Therefore, it is necessary to explore the connection between coal mine workers’ neural activity and unsafe behavior from the perspective of cognitive neuroscience. This study explored the functional brain connectivity of coal mine workers who have engaged in unsafe behaviors (EUB) and those who have not (NUB). (2) Methods: Based on functional near-infrared spectroscopy (fNIRS), a total of 106 workers from the Hongliulin coal mine of Shaanxi North Mining Group, one of the largest modern coal mines in China, completed the test. Pearson’s Correlation Coefficient (COR) analysis, brain network analysis, and two-sample t-test were used to investigate the difference in brain functional connectivity between the two groups. (3) Results: The results showed that there were significant differences in functional brain connectivity between EUB and NUB among the frontopolar area (p = 0.002325), orbitofrontal area (p = 0.02102), and pars triangularis Broca’s area (p = 0.02888). Small-world properties existed in the brain networks of both groups, and the dorsolateral prefrontal cortex had significant differences in clustering coefficient (p = 0.0004), nodal efficiency (p = 0.0384), and nodal local efficiency (p = 0.0004). (4) Conclusions: This study is the first application of fNIRS to the field of coal mine safety. The fNIRS brain functional connectivity analysis is a feasible method to investigate the neuropsychological mechanism of unsafe behavior in coal mine workers in the view of brain science.

Sex-Specific Brain Responses to Imaginary Dance but Not Physical Dance: An Electroencephalography Study of Functional Connectivity and Electrical Brain Activity.

To date, most neurophysiological dance research has been conducted exclusively with female participants in observational studies (i.e., participants observe or imagine a dance choreography). In this regard, the sex-specific acute neurophysiological effect of physically executed dance can be considered a widely unexplored field of research. This study examines the acute impact of a modern jazz dance choreography on brain activity and functional connectivity using electroencephalography (EEG). In a within-subject design, 11 female and 11 male participants were examined under four test conditions: physically dancing the choreography with and without music and imagining the choreography with and without music. Prior to the EEG measurements, the participants acquired the choreography over 3 weeks with one session per week. Subsequently, the participants conducted all four test conditions in a randomized order on a single day, with the EEG measurements taken before and after each condition. Differences between the male and female participants were established in brain activity and functional connectivity analyses under the condition of imagined dance without music. No statistical differences between sexes were found in the other three conditions (physically executed dance with and without music as well as imagined dance with music). Physically dancing and music seem to have sex-independent effects on the human brain. However, thinking of dance without music seems to be rather sex-specific. The results point to a promising approach to decipher sex-specific differences in the use of dance or music. This approach could further be used to achieve a more group-specific or even more individualized and situationally adapted use of dance interventions, e.g., in the context of sports, physical education, or therapy. The extent to which the identified differences are due to culturally specific attitudes in the sex-specific contact with dance and music needs to be clarified in future research.

Combining power of different methods to detect associations in large data sets.

Exploring the relationship between factors of interest is a fundamental step for further analysis on various scientific problems such as understanding the genetic mechanism underlying specific disease, brain functional connectivity analysis. There are many methods proposed for association analysis and each has its own advantages, but none of them is suitable for all kinds of situations. This brings difficulties and confusions to practitioner on which one to use when facing a real problem. In this paper, we propose to combine power of different methods to detect associations in large data sets. It goes as combining the weaker to be stronger. Numerical results from simulation study and real data applications show that our new framework is powerful. Importantly, the framework can also be applied to other problems. Availability: The R script is available at https://jiangdata.github.io/resources/DM.zip.

Brain atrophy progression in Parkinson's disease is shaped by connectivity and local vulnerability.

Brain atrophy has been reported in the early stages of Parkinson’s disease, but there have been few longitudinal studies. How intrinsic properties of the brain, such as anatomical connectivity, local cell-type distribution and gene expression combine to determine the pattern of disease progression also remains unknown. One hypothesis proposes that the disease stems from prion-like propagation of misfolded alpha-synuclein via the connectome that might cause varying degrees of tissue damage based on local properties. Here, we used MRI data from the Parkinson Progression Markers Initiative to map the progression of brain atrophy over 1, 2 and 4 years compared with baseline. We derived atrophy maps for four time points using deformation-based morphometry applied to T1-weighted MRI from 120 de novo Parkinson’s disease patients, 74 of whom had imaging at all four time points (50 Men: 24 Women) and 157 healthy control participants (115 Men: 42 Women). In order to determine factors that may influence neurodegeneration, we related atrophy progression to brain structural and functional connectivity, cell-type expression and gene ontology enrichment analyses. After regressing out the expected age and sex effects associated with normal ageing, we found that atrophy significantly progressed over 2 and 4 years in the caudate, nucleus accumbens, hippocampus and posterior cortical regions. This progression was shaped by both structural and functional brain connectivity. Also, the progression of atrophy was more pronounced in regions with a higher expression of genes related to synapses and was inversely related to the prevalence of oligodendrocytes and endothelial cells. In sum, we demonstrate that the progression of atrophy in Parkinson’s disease is in line with the prion-like propagation hypothesis of alpha-synuclein and provide evidence that synapses may be especially vulnerable to synucleinopathy. In addition to identifying vulnerable brain regions, this study reveals different factors that may be implicated in the neurotoxic mechanisms leading to progression in Parkinson’s disease. All brain maps generated here are available on request.

Quantified assessment of deep brain stimulation on Parkinson\u2019s patients with task fNIRS measurements and functional connectivity analysis: a pilot study

BackgroundDeep brain stimulation (DBS) has proved effective for Parkinson’s disease (PD), but the identification of stimulation parameters relies on doctors’ subjective judgment on patient behavior.MethodsFive PD patients performed 10-meter walking tasks under different brain stimulation frequencies. During walking tests, a wearable functional near-infrared spectroscopy (fNIRS) system was used to measure the concentration change of oxygenated hemoglobin (△HbO2) in prefrontal cortex, parietal lobe and occipital lobe. Brain functional connectivity and global efficiency were calculated to quantify the brain activities.ResultsWe discovered that both the global and regional brain efficiency of all patients varied with stimulation parameters, and the DBS pattern enabling the highest brain efficiency was optimal for each patient, in accordance with the clinical assessments and DBS treatment decision made by the doctors.ConclusionsTask fNIRS assessments and brain functional connectivity analysis promise a quantified and objective solution for patient-specific optimization of DBS treatment.Trial registrationName: Accurate treatment under the multidisciplinary cooperative diagnosis and treatment model of Parkinson’s disease. Registration number is ChiCTR1900022715. Date of registration is April 23, 2019.

Testing Graph Robustness Indexes for EEG Analysis in Alzheimer’s Disease Diagnosis

Alzheimer’s Disease (AD) is an incurable neurodegenerative disorder which mainly affects older adults. An early diagnosis is essential because medical treatments can slow down the progression of the disease only if provided during the first stage, called Mild Cognitive Impairment (MCI). Starting from the study of electroencephalografic signals, brain functional connectivity analyses can be performed with the support of the graph theory. In particular, the purpose of this work is to verify the performances of three indexes, typically adopted to evaluate the graph robustness, in order to estimate the functional connectivity for three groups of subjects: healthy controls and people affected by dementia at two different stages (MCI and AD). The results obtained by the Connection Density Index, the Randić Index, and a normalized version of the Kirchhoff Index revealed a higher robustness in the brain networks of healthy people, followed by MCI and, finally, by AD patients, consistent with the hallmarks of Alzheimer’s disease. The statistical analysis showed that there is a significant difference between controls and AD for all three indexes. Finally, all three indexes were compared, revealing that the the Randić Index outperformed the other two indexes. These preliminary outcomes will be exploited to address further in-depth and time-expensive analyses for improving the diagnosis of Alzheimer’s disease.

Phase fMRI defines brain resting-state functional hubs within central and posterior regions.

From a brain functional connectivity (FC) matrix, we can identify the hub nodes by a new method of eigencentrality mapping, which not only counts for one node's centrality but also all other nodes' centrality values through correlation connections in an eigenvector of the FC matrix. For the resting-state functional MRI (fMRI) data (complex-valued EPI images in nature), both magnitude and phase images are useful for brain FC analysis. We herein report on brain functional hubness analysis by constructing the FC matrix from phase fMRI data and identifying the hub nodes by eigencentrality mapping. In our study, we collected a cohort of 160 complex-valued fMRI dataset (consisting of magnitude and phase in pairs), and performed independent component analysis (ICA), FC matrix calculation (in size of 50 × 50) and FC matrix eigen decomposition; thereby obtained the 50-node eigencentrality values in the eigenvector associated with the largest eigenvalue. We also compared the hub structures inferred from FC matrices under different thresholding. Alternatively, we obtained the geometric hubs among p value the 50 nodes involved in the FC matrix through the use of harmonic centrality metric. Our results showed that phase fMRI data analysis defines the resting-state brain functional hubs primarily in the central region (subcortex) and the posterior region (parieto-occipital lobes and cerebella). The brain central hubness was supported by the geometric central hubness, which, however, is distinct from the magnitude-inferred hubness in brain superior region (frontal and parietal lobes). Our findings pose a new understanding of (or a debate over) brain functional connectivity architecture.

Brain functional connectivity analysis based on multi-graph fusion.

In this paper, we propose a framework for functional connectivity network (FCN) analysis, which conducts the brain disease diagnosis on the resting state functional magnetic resonance imaging (rs-fMRI) data, aiming at reducing the influence of the noise, the inter-subject variability, and the heterogeneity across subjects. To this end, our proposed framework investigates a multi-graph fusion method to explore both the common and the complementary information between two FCNs, i.e.,a fully-connected FCN and a 1 nearest neighbor (1NN) FCN, whereas previous methods only focus on conducting FCN analysis from a single FCN. Specifically, our framework first conducts the graph fusion to produce the representation of the rs-fMRI data with high discriminative ability, and then employs the L1SVM to jointly conduct brain region selection and disease diagnosis. We further evaluate the effectiveness of the proposed framework on various data sets of the neuro-diseases, i.e.,Fronto-Temporal Dementia (FTD), Obsessive-Compulsive Disorder (OCD), and Alzheimers Disease (AD). The experimental results demonstrate that the proposed framework achieves the best diagnosis performance via selecting reasonable brain regions for the classification tasks, compared to state-of-the-art FCN analysis methods.

The Differences in the Whole-Brain Functional Network between Cantonese-Mandarin Bilinguals and Mandarin Monolinguals.

Cantonese-Mandarin bilinguals are logographic-logographic bilinguals that provide a unique population for bilingual studies. Whole brain functional connectivity analysis makes up for the deficiencies of previous bilingual studies on the seed-based approach and helps give a complete picture of the brain connectivity profiles of logographic-logographic bilinguals. The current study is to explore the effect of the long-term logographic-logographic bilingual experience on the functional connectivity of the whole-brain network. Thirty Cantonese-Mandarin bilingual and 30 Mandarin monolingual college students were recruited in the study. Resting state functional magnetic resonance imaging (rs-fMRI) was performed to investigate the whole-brain functional connectivity differences by network-based statistics (NBS), and the differences in network efficiency were investigated by graph theory between the two groups (false discovery rate corrected for multiple comparisons, q = 0.05). Compared with the Mandarin monolingual group, Cantonese-Mandarin bilinguals increased functional connectivity between the bilateral frontoparietal and temporal regions and decreased functional connectivity in the bilateral occipital cortex and between the right sensorimotor region and bilateral prefrontal cortex. No significant differences in network efficiency were found between the two groups. Compared with the Mandarin monolinguals, Cantonese-Mandarin bilinguals had no significant discrepancies in network efficiency. However, the Cantonese-Mandarin bilinguals developed a more strongly connected subnetwork related to language control, inhibition, phonological and semantic processing, and memory retrieval, whereas a weaker connected subnetwork related to visual and phonology processing, and speech production also developed.

A Revised Hilbert-Huang Transformation to Track Non-Stationary Association of Electroencephalography Signals.

The time-varying cross-spectrum method has been used to effectively study transient and dynamic brain functional connectivity between non-stationary electroencephalography (EEG) signals. Wavelet-based cross-spectrum is one of the most widely implemented methods, but it is limited by the spectral leakage caused by the finite length of the basic function that impacts the time and frequency resolutions. This paper proposes a new time-frequency brain functional connectivity analysis framework to track the non-stationary association of two EEG signals based on a Revised Hilbert-Huang Transform (RHHT). The framework can estimate the cross-spectrum of decomposed components of EEG, followed by a surrogate significance test. The results of two simulation examples demonstrate that, within a certain statistical confidence level, the proposed framework outperforms the wavelet-based method in terms of accuracy and time-frequency resolution. A case study on classifying epileptic patients and healthy controls using interictal seizure-free EEG data is also presented. The result suggests that the proposed method has the potential to better differentiate these two groups benefiting from the enhanced measure of dynamic time-frequency association.

Resting State Functional MRI in Neonates with Prenatal Opioid Exposure: Analysis of Thalamocortical Functional Connectivity

Background/Objective: Prenatal opioid exposure (POE) is a growing public health issue that can result in premature birth, Neonatal Abstinence Syndrome (NAS), and adverse neurodevelopmental outcomes. However, the neural basis for these findings remains relatively unknown. In this study, we aimed to investigate the neural correlates of POE based on neonatal thalamocortical functional connectivity using resting state functional magnetic resonance imaging (rs-fMRI). &#x0D; &#x0D; Methods: In this prospective, IRB-approved study, nineteen neonates with POE and twenty opioid naive (ON) controls underwent non-invasive MRI during natural sleep at mean post-menstrual age (PMA) of 44.7 ± 2.6 and 44.6 ± 2.6 weeks respectively. MR imaging included anatomic T2-weighted images and rs-fMRI. General Linear Model (GLM) seed-based whole brain functional connectivity analysis was performed for each subject, with the right and left thalamus as distinct seed regions. Unpaired mixed-effects group analyses between POE and ON groups were conducted for each seed region corrected for PMA and sex. &#x0D; &#x0D; Results: Thalamic connectivity to cortical and subcortical structures differed in the POE group compared to the ON control group. The POE group exhibited higher functional connectivity to deep gray structures, frontal, medial prefrontal, parietal, occipital, and anterior temporal cortices compared to controls. The POE group exhibited lower connectivity to the nuclei accumbentes, bilateral caudate nuclei, posterior cingulate gyri, superior frontal gyri, insular, and dorsolateral prefrontal cortices. &#x0D; &#x0D; Conclusion and Potential Impact: Overall, these novel results suggest the presence of opioid exposure-related alterations in thalamic functional connectivity. Given that the thalamus plays a crucial role in early brain development, the described alterations in thalamocortical and thalamic-subcortical connectivity may have implications in stratifying risk and informing treatment for the adverse neurodevelopmental outcomes associated with POE. Future studies should explore the relationship between POE-associated disruptions in thalamic connectivity and developmental outcomes.

Modulation of the functional connectome in major depressive disorder by ketamine therapy.

Subanesthetic ketamine infusion therapy can produce fast-acting antidepressant effects in patients with major depression. How single and repeated ketamine treatment modulates the whole-brain functional connectome to affect clinical outcomes remains uncharacterized. Data-driven whole brain functional connectivity (FC) analysis was used to identify the functional connections modified by ketamine treatment in patients with major depressive disorder (MDD). MDD patients (N = 61, mean age = 38, 19 women) completed baseline resting-state (RS) functional magnetic resonance imaging and depression symptom scales. Of these patients, n = 48 and n = 51, completed the same assessments 24 h after receiving one and four 0.5 mg/kg intravenous ketamine infusions. Healthy controls (HC) (n = 40, 24 women) completed baseline assessments with no intervention. Analysis of RS FC addressed effects of diagnosis, time, and remitter status. Significant differences (p < 0.05, corrected) in RS FC were observed between HC and MDD at baseline in the somatomotor network and between association and default mode networks. These disruptions in FC in MDD patients trended toward control patterns with ketamine treatment. Furthermore, following serial ketamine infusions, significant decreases in FC were observed between the cerebellum and salience network (SN) (p < 0.05, corrected). Patient remitters showed increased FC between the cerebellum and the striatum prior to treatment that decreased following treatment, whereas non-remitters showed the opposite pattern. Results support that ketamine treatment leads to neurofunctional plasticity between distinct neural networks that are shown as disrupted in MDD patients. Cortico-striatal-cerebellar loops that encompass the SN could be a potential biomarker for ketamine treatment.

A Comparative Study of the Effect of Weighted or Binary Functional Brain Networks in fMRI Data Analysis

Purpose: Graph theory is a widely used and reliable tool to quantify brain connectivity. Brain functional connectivity is modeled as graph edges employing correlation coefficients. The correlation coefficients can be used as the weight that shows the power of connectivity between two nodes or can be binarized to show the existence of a connection regardless of its strength. To binarize the brain graph two approaches, namely fixed threshold and fixed density are often used. Materials and Methods: This paper aims to investigate the difference between weighted or binarized graphs in brain functional connectivity analysis. To achieve this goal, the brain connectivity matrices are generated employing the functional Magnetic Resonance Imaging (fMRI) data of Alzheimer's Disease (AD). After preprocessing the data, weighted and binarized connectivity matrices are constructed using a fixed threshold and fixed density techniques. Graph global features are extracted and a non-parametric statistical test is performed to analyze the performance of the methods. Results: Results show that all three methods are powerful in distinguishing the healthy group from AD subjects. The P-Values of the weighted graph is close to the fixed threshold method. Conclusion: Also, it is worthwhile mentioning that the fixed threshold method is robust in changing the threshold while the fixed density method is very sensitive. On the other hand, graph global measures such as clustering coefficient and transitivity, regardless of the method, show significant differences between the control and AD groups. Furthermore, the P-Values of modularity measure are very varied according to the method and the selected threshold.