Structural Brain Connectivity Research Articles

Detecting influential observations in single-index Fréchet regression

Regression with random data objects is becoming increasingly common in modern data analysis. Unfortunately, this novel regression method is not immune to the trouble caused by unusual observations. A metric Cook’s distance extending the original Cook’s distances of Cook (1977) to regression between metric-valued response objects and Euclidean predictors is proposed. The performance of the metric Cook’s distance is demonstrated in regression across four different response spaces in an extensive experimental study. Two real data applications involving the analysis of distributions of COVID-19 transmission in the State of Texas and the analysis of the structural brain connectivity networks are provided to illustrate the utility of the proposed method in practice.

Individual Variability in the Structural Connectivity Architecture of the Human Brain.

The human brain exhibits high degree of individual variability in both its structure and function, which underlies inter-subject differences in cognition and behavior. It was previously shown that functional connectivity is more variable in the hetero-modal association cortex but less variable in the unimodal cortices. Structural connectivity is the anatomical substrate of functional connectivity, but the spatial and temporal patterns of individual variability in structural connectivity (IVSC) remain largely unknown. In the present study, we discovered a detailed and robust chart of IVSC obtained by applying diffusion MRI and tractography techniques to 1724 adults (770 males and 954 females) from multiple imaging datasets. Our results showed that the structural connectivity exhibited the highest and lowest variability in the limbic regions and the unimodal sensorimotor regions, respectively. With increased age, higher IVSC was observed across most brain regions. Moreover, the specific spatial distribution of IVSC is related to the cortical laminar differentiation and myelination content. Finally, we proposed a modified ridge regression model to predict individual cognition and generated idiographic brain mapping, which was significantly correlated with the spatial pattern of IVSC. Overall, our findings further contribute to the understanding of the mechanisms of individual variability in brain structural connectivity and link to the prediction of individual cognitive function in adult subjects.Significance Statement White matter connectivity between grey matter regions plays an important role in integrating information from distributed regions when individuals performing complex cognitive functions. Unique white matter connectivity is the neuroanatomical identity of each individual. The authors systematically explored the spatio-temporal pattern of individual variability in structural connectivity, and the results show higher variability in structural connectivity relates to higher neuroplasticity. In addition, this study reveals that structural connectivity involved in executive function and attention task is different among individuals and highlights the importance of individualized statistical methods for mapping neural pathway of complex cognition.

Genetically Supported Causality between Brain Structural Connectome and Sleep Duration in Children: A Two-Sample Mendelian Randomization Study.

Certain structural brain connections have been confirmed to influence sleep duration in children. However, the causal relationships between all brain regions and children's sleep duration remain unclear. A two-sample Mendelian randomization analysis was conducted using data from genome-wide association studies (GWAS) to examine the relationships between 206 structural connections and sleep duration in children. Sensitivity analyses were employed to validate the findings and assess the robustness of the causal inferences. Stronger connectivity from the left hemisphere (LH) control network to the accumbens (β = -0.15; 95% CI = [-0.30, -2.88 × 10-3]; p = 0.05) and from the LH somatomotor network to the LH default network (β = -0.18; 95% CI = [-0.34, -0.03]; p = 0.02) in white-matter structural connectivity (SC) were associated with shorter sleep durations. Conversely, increased white-matter SC from the LH dorsal attention network to the thalamus (β = 0.14; 95% CI = [8.45 × 10-4, 0.27]; p = 0.05), from the right hemisphere (RH) control network to the thalamus (β = 0.10; 95% CI = [0.01, 0.19]; p = 0.03), from the RH default network to the thalamus (β = 0.08; 95% CI = [4.53 × 10-3, 0.16]; p = 0.04), from the RH limbic network to the thalamus (β = 0.15; 95% CI = [0.05, 0.26]; p = 3.77 × 10-3), and from the RH somatomotor network to the thalamus (β = 0.20; 95% CI = [0.07, 0.32]; p = 1.63 × 10-3) correlated with longer sleep durations in children. Two-sample Mendelian randomization provides novel insights into the relationships between brain regions and sleep duration in children. Our findings demonstrate a causal relationship between specific brain areas and sleep duration.

Olfactory training affects the correlation between brain structure and functional connectivity.

and background: Neuroimaging studies have increasingly found functional connectivity (FC) changes and structural cortical abnormalities in patients with post-traumatic anosmia (PTA). Training and repeated exposure to odorants lead to enhanced olfactory capability. This study is conducted to investigate the correlations between FC and cortical thickness on the olfaction-related regions of the brain in PTA after olfactory training (OT). Twenty-five PTA patients were randomly divided in three groups: (1) 9 control patients who did not receive any training, (2) 9 patients underwent classical OT by 4 fixed odors, and (3) 7 patients underwent modified OT coming across 4 sets of 4 different odors sequentially. Before and after the training period, all patients performed olfactory function tests, and magnetic resonance imaging (MRI). Sniffin' Sticks test was used to assess olfactory function. MRI data were analyzed using functional connectivity analysis and brain morphometry. Modified OT resulted in heightened activation in the medial orbitofrontal cortex and anterior cingulate cortex and increased FC between the piriform cortex (PIRC) and the caudate cortex. Conversely, classical OT induced increased activation in the insula cortex and greater FC between the PIRC and the pre-central gyrus. Furthermore, after OT, both training groups achieved significantly improved scores in the changes in brain connectivity associated with OT, which were attributable to anatomical measures. This study demonstrates that intensive olfactory training can enhance functional connectivity, and this improvement correlates with structural changes in the brain's olfactory processing areas.

Structural brain connectivity does not associate with childhood trauma in individuals with schizophrenia

BackgroundSchizophrenia is a brain dysconnectivity disorder. However, it is not well understood whether the experience of childhood trauma (CT) affects dysconnectivity in individuals with schizophrenia (SZ). Using a network-based approach, we examined whether self-reported CT would explain additional variance compared to whole-brain topology and structural connectivity changes in SZ versus healthy controls (HC). Material and methodsCT was assessed in 51 SZ (mean age ± standard deviation 44 ± 11 years) and 140 HC (34.0 ± 12 years). Structural brain networks were constructed from T1-weighted MR and diffusion-MRI scans using non-tensor based tractography. Group differences in whole-brain topology and permutation-based statistics were examined and corrected for age and sex. ResultsSZ showed reductions in efficiency, strength, clustering and density (p <0.01) as well as increases in path length (F(range) = 4.71-18.1, p <0.03) when compared to HC. We also observed hypoconnectivity in a subnetwork of frontotemporal, frontoparietal and occipital regions in SZ relative to HC (T > 4.0, p <0.001). However, we did not find that high CT levels were related to structural network differences or structural connectivity changes in SZ. ConclusionsCT did not impact on topology or subnetwork connectivity changes in SZ. High CT levels were also not associated with any differences in network organisation irrespective of diagnosis. However, our findings confirm that SZ showed both network-level reductions and increases in a subnetwork. These findings suggest that the patterns of neuroanatomical dysconnectivity in established schizophrenia may not be influenced by CT. Future studies are needed to investigate the association between CT and structural dysconnectivity in schizophrenia.

A Multivariate and Network Analysis Uncovers a Long-Term Influence of Exclusive Breastfeeding on the Development of Brain Morphology and Structural Connectivity.

Exclusive breastfeeding (eBF) in infancy appears to offer a developmental advantage for children's brains compared to formula-fed counterparts. Existing research has predominantly focused on global brain measures (i.e., total white/grey matter volumes) or on limited sets of specific brain regions, in selected age groups, leaving uncertainties about the impact of eBF on the overall structural connectomes. In this cross-sectional study encompassing participants from childhood to adulthood, partial least squares correlations (PLSC) were employed to assess white and grey matter volumes. Furthermore, a network analytic approach was used to estimate the structural connectome based on cortical thickness data. The results revealed that eBF duration correlated with increased white matter volumes in children and with the volume of the medial orbital gyrus in adults. Structural connectome analyses demonstrated heightened anatomical connectivity in eBF children, evidenced by enhanced network density and local/global efficiency, along with increased node degree and local efficiency in frontal and temporal lobes. Similarly, eBF in adults was associated to an improved node connectivity in the frontal lobe. These findings imply a lasting impact of eBF on brain morphometry and structural connectivity. Childhood benefits include heightened white matter development, while in adulthood, eBF may contribute to reduced neural loss associated with aging and enhanced connectivity, particularly in frontal regions.

Resting-State Functional Magnetic Resonance Imaging Reveals the Effects of rTMS on Neural Activity and Brain Connectivity After Experimental Stroke.

Limited understanding of neurobiological mechanisms of repetitive transcranial magnetic stimulation (rTMS) prevents us from choosing optimal therapeutic regimen for patients to improve therapeutic efficiency. Resting-state functional magnetic resonance imaging (rs-fMRI) has been demonstrated to obtain comparable functional readouts across species. Intermittent and continuous theta burst stimulation were used to stimulate ipsilesional and contralesional hemisphere, respectively, during the subacute phase after stroke. We used a rat middle cerebral artery occlusion stroke model. The amplitude of low-frequency fluctuations and functional connectivity analyses of rs-fMRI were chosen to detect neuron activity and functional connectivity. The expression of neuron activation marker c-Fos and axonal plasticity marker GAP43 was examined by an immunochemistry method to corroborate the results of rs-fMRI. iTBS altered the long-term neuronal activity in bilateral sensorimotor cortex, whereas cTBS influenced immediate neuronal activity of bilateral sensorimotor cortex. In addition, cTBS enhanced interhemispheric and intrahemisheric functional connectivity in contralesional hemisphere, accompanied by axonal and dendritic remodeling in the perilesional cortical areas and contralesional homologous areas after large stroke. rTMS exerted complex effects on brain structural and functional connectivity in addition to affecting cortical excitability. cTBS promoted the compensatory effect of contralesional hemisphere after stroke with large lesions.

Spherical-deconvolution informed filtering of tractograms changes laterality of structural connectome

Diffusion MRI-driven tractography, a non-invasive technique that reveals how the brain is connected, is widely used in brain lateralization studies. To improve the accuracy of tractography in showing the underlying anatomy of the brain, various tractography filtering methods were applied to reduce false positives. Based on different algorithms, tractography filtering methods are able to identify the fibers most consistent with the original diffusion data while removing fibers that do not align with the original signals, ensuring the tractograms are as biologically accurate as possible. However, the impact of tractography filtering on the lateralization of the brain connectome remains unclear. This study aims to investigate the relationship between fiber filtering and laterality changes in brain structural connectivity. Three typical tracking algorithms were used to construct the raw tractography, and two popular fiber filtering methods(SIFT and SIFT2) were employed to filter the tractography across a range of parameters. Laterality indices were computed for six popular biological features, including four microstructural measures (AD, FA, RD, and T1/T2 ratio) and two structural features (fiber length and connectivity) for each brain region. The results revealed that tractography filtering may cause significant laterality changes in more than 10% of connections, up to 25% for probabilistic tracking, and deterministic tracking exhibited minimal laterality changes compared to probabilistic tracking, experiencing only about 6%. Except for tracking algorithms, different fiber filtering methods, along with the various biological features themselves, displayed more variable patterns of laterality change. In conclusion, this study provides valuable insights into the intricate relationship between fiber filtering and laterality changes in brain structural connectivity. These findings can be used to develop improved tractography filtering methods, ultimately leading to more robust and reliable measurements of brain asymmetry in lateralization studies.

Enhancing daily life for children with cognitive developmental delay through insights into brain development.

Cognitive developmental delay, including severe intellectual disability (IQ below 70) and borderline intellectual functioning (IQ 70-85), poses significant challenges, including high costs and emotional burden. Early diagnosis and interventions might improve adaptive behavior and daily life functioning. High-risk groups include children with neonatal complications, congenital anomalies, genetic disorders, or metabolic errors, yet over 50% of cases have unknown causes. To provide timely diagnosis and intervention for children with cognitive developmental delay, it is important to increase our understanding and ability to prognosticate their level of functioning. The pivotal role of brain development in the first few years of life presents a window of opportunity for these goals. By detailed investigation of common patterns in structural brain development and connectivity by MRI in relation to cognitive and executive functioning, this review aims to identify potential factors that might improve understanding and prognostication of children with cognitive developmental delay. Exploring similarities among diverse patient groups with childhood cognitive developmental delay, this review intends to provide a nuanced perspective. IMPACT: This review identified several MRI brain developmental markers, especially in the white matter, that might hold potential to be a prognostic marker for intellectual and executive functioning in children with cognitive developmental delay. Bringing together information on aberrant brain developmental trajectories and connectivity across different patient childhood populations with cognitive developmental delay might improve our understanding and prognostication.

Association Between Glaucoma and Brain Structural Connectivity Based on Diffusion Tensor Tractography: A Bidirectional Mendelian Randomization Study.

Glaucoma is a neurodegenerative ocular disease that is accompanied by cerebral damage extending beyond the visual system. Recent studies based on diffusion tensor tractography have suggested an association between glaucoma and brain structural connectivity but have not clarified causality. To explore the causal associations between glaucoma and brain structural connectivity, a bidirectional Mendelian randomization (MR) study was conducted involving glaucoma and 206 diffusion tensor tractography traits. Highly associated genetic variations were applied as instrumental variables and statistical data were sourced from the database of FinnGen and UK Biobank. The inverse-variance weighted method was applied to assess causal relationships. Additional sensitivity analyses were also performed. Glaucoma was potentially causally associated with alterations in three brain structural connectivities (from the SN to the thalamus, from the DAN to the putamen, and within the LN network) in the forward MR analysis, whereas the inverse MR results identified thirteen brain structural connectivity traits with a potential causal relationship to the risk of glaucoma. Both forward and reverse MR analyses satisfied the sensitivity test with no significant horizontal pleiotropy or heterogeneity. This study offered suggestive evidence for the potential causality between the risk of glaucoma and brain structural connectivity. Our findings also provided novel insights into the neurodegenerative mechanism of glaucoma.

Atlas-Based Structural Disconnectomes Are Associated with Cognitive Performance in Brain Tumors.

Background: Brain tumors are associated with impaired cognitive functioning, which may result from disruptions in brain structural connectivity. Estimating structural disconnections is a more advantageous representation of tumor impact and can be performed indirectly through normative brain atlases. Materials and Methods: Using a publicly available dataset of glioma and meningioma patient MRI scans and tumor masks, latent correlations were estimated between measures of structural disconnection and attention-based cognitive functioning. These measures included gray matter (GM) parcel damage, white matter tract damage, GM parcel-to-parcel disconnections, and reaction time (RTI) as part of the Cambridge Neuropsychological Test Automated Battery to assess attention. Results: Preprocessing pipelines with two different methods of minimizing the pathology impact on MRI normalization were utilized: cost-function masking and lesion filling. The results across both pipelines were nearly consistent, with significant correlations mainly found between RTI measures and the damage to the left inferior fronto-occipital and uncinate fasciculus, as well as the left prefrontal-visual disconnections. Conclusions: This alludes to the importance of left-hemispheric prefrontal-visual coupling in attention-based tasks, particularly those involving object- and feature-based attention.

Bayesian inference of frequency-specific functional connectivity in MEG imaging using a spectral graph model

Abstract Understanding the relationship between structural connectivity (SC) and functional connectivity (FC) of the human brain is an important goal of neuroscience. Highly detailed mathematical models of neural masses exist that can simulate the interactions between functional activity and structural wiring. These models are often complex and require intensive computation. Most importantly, they do not provide a direct or intuitive interpretation of this structure–function relationship. In this study, we employ the emerging concepts of spectral graph theory to obtain this mapping in terms of graph harmonics, which are eigenvectors of the structural graph’s Laplacian matrix. In order to imbue these harmonics with biophysical underpinnings, we leverage recent advances in parsimonious spectral graph modeling (SGM) of brain activity. Here, we show that such a model can indeed be cast in terms of graph harmonics, and can provide a closed-form prediction of FC in an arbitrary frequency band. The model requires only three global, spatially invariant parameters, yet is capable of generating rich FC patterns in different frequency bands. Only a few harmonics are sufficient to reproduce realistic FC patterns. We applied the method to predict FC obtained from pairwise magnitude coherence of source-reconstructed resting-state magnetoencephalography (MEG) recordings of 36 healthy subjects. To enable efficient model inference, we adopted a deep neural network-based Bayesian procedure called simulation-based inference. Using this tool, we were able to speedily infer not only the single most likely model parameters, but also their full posterior distributions. We also implemented several other benchmark methods relating SC to FC, including graph diffusion and coupled neural mass models. The present method was shown to give the best performance overall. Notably, we discovered that a single biophysical parameterization is capable of fitting FCs from all relevant frequency bands simultaneously, an aspect that did not receive adequate attention in prior computational studies.

Conservation of structural brain connectivity in people with multiple sclerosis

ABSTRACT Multiple sclerosis (MS) is a neurodegenerative disease that affects the central nervous system. Structures affected in MS include the corpus callosum, connecting the hemispheres. Studies have shown that in mammalian brains, structural connectivity is organized according to a conservation principle, an inverse relationship between intra- and interhemispheric connectivity. The aim of this study was to replicate this conservation principle in subjects with MS and to explore how the disease interacts with it. A multicentric dataset has been analyzed including 513 people with MS and 208 healthy controls from seven different centers. Structural connectivity was quantified through various connectivity measures, and graph analysis was used to study the behavior of intra- and interhemispheric connectivity. The association between the intra- and the interhemispheric connectivity showed a similar strength for healthy controls (r = 0.38, p &amp;lt; 0.001) and people with MS (r = 0.35, p &amp;lt; 0.001). Intrahemispheric connectivity was associated with white matter fraction (r = 0.48, p &amp;lt; 0.0001), lesion volume (r = −0.44, p &amp;lt; 0.0001), and the Symbol Digit Modalities Test (r = 0.25, p &amp;lt; 0.0001). Results show that this conservation principle seems to hold for people with MS. These findings support the hypothesis that interhemispheric connectivity decreases at higher cognitive decline and disability levels, while intrahemispheric connectivity increases to maintain the balance.

Discovering prominent differences in structural and functional connectomes using a multinomial stochastic block model

ABSTRACT Understanding the differences between functional and structural human brain connectivity has been a focus of an extensive amount of neuroscience research. We employ a novel approach using the multinomial stochastic block model (MSBM) to explicitly extract components that characterize prominent differences across graphs. We analyze structural and functional connectomes derived from high-resolution diffusion-weighted MRI and fMRI scans of 250 Human Connectome Project subjects, analyzed at group connectivity level across 50 subjects. The inferred brain partitions revealed consistent, spatially homogeneous clustering patterns across inferred resolutions demonstrating the MSBM’s reliability in identifying brain areas with prominent structure-function differences. Prominent differences in low-resolution brain maps (K = {3, 4} clusters) were attributed to weak functional connectivity in the bilateral anterior temporal lobes, while higher resolution results (K ≥ 25) revealed stronger interhemispheric functional than structural connectivity. Our findings emphasize significant differences in high-resolution functional and structural connectomes, revealing challenges in extracting meaningful connectivity measurements from both modalities, including tracking fibers through the corpus callosum and attenuated functional connectivity in anterior temporal lobe fMRI data, which we attribute to increased noise levels. The MSBM emerges as a valuable tool for understanding differences across graphs, with potential future applications and avenues beyond the current focus on characterizing modality-specific distinctions in connectomics data.

A spatially constrained independent component analysis jointly informed by structural and functional network connectivity

Abstract There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. The brain connectivity of different modalities provides an insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multimodal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model estimates ICNs at the subject level using a multiobjective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multimodal ICNs revealed enhanced functional coupling between ICNs with higher structural connectivity, improved modularity, and network distinction, particularly in schizophrenia. Statistical analysis of group differences showed more significant differences in the proposed model compared with the unimodal model. In summary, the sfCICA model showed benefits from being jointly informed by structural and functional connectivity. These findings suggest advantages in simultaneously learning effectively and enhancing connectivity estimates using structural connectivity.

Nerve agent exposure and physiological stress alter brain microstructure and immune profiles after inflammatory challenge in a long-term rat model of Gulf War Illness

Gulf War Illness (GWI) is a disorder experienced by many veterans of the 1991 Gulf War, with symptoms including fatigue, chronic pain, respiratory and memory problems. Exposure to toxic chemicals during the war, such as oil well fire smoke, pesticides, physiological stress, and nerve agents, is thought to have triggered abnormal neuroinflammatory responses that contribute to GWI. Previous studies have examined the acute effects of combined physiological stress and chemical exposures using GWI rodent models and presented findings related to neuroinflammation and changes in diffusion magnetic resonance imaging (MRI) measures, suggesting a neuroimmune basis for GWI. In the current study, using ex vivo MRI, cytokine mRNA expression, and immunohistological analyses of brain tissues, we examined the brain structure and immune function of a chronic rat model of GWI. Our data showed that a combination of long-term corticosterone treatment (to mimic high physiological stress) and diisopropyl fluorophosphate exposure (to mimic sarin exposure) primed the response to subsequent systemic immune challenge with lipopolysaccharide resulting in elevations of multiple cytokine mRNAs, an increased activated glial population, and disrupted brain microstructure in the cingulate cortex and hippocampus compared to control groups. Our findings support the critical role of neuroinflammation, dysregulated glial activation, and their relationship to disrupted brain microstructural integrity in the pathophysiology of GWI and highlight the unique consequences of long-term combined exposures on brain biochemistry and structural connectivity.

Diagnosis of Major Depressive Disorder Based on Individualized Brain Functional and Structural Connectivity.

Traditional neuroimaging studies have primarily emphasized analysis at the group level, often neglecting the specificity at the individual level. Recently, there has been a growing interest in individual differences in brain connectivity. Investigating individual-specific connectivity is important for understanding the mechanisms of major depressive disorder (MDD) and the variations among individuals. To integrate individualized functional connectivity and structural connectivity with machine learning techniques to distinguish people with MDD and healthy controls (HCs). Prospective. A total of 182 patients with MDD and 157 HCs and a verification cohort including 54 patients and 46 HCs. 3.0 T/T1-weighted imaging, resting-state functional MRI with echo-planar sequence, and diffusion tensor imaging with single-shot spin echo. Functional and structural brain networks from rs-fMRI and DTI data were constructed, respectively. Based on these networks, individualized functional connectivity (IFC) and individualized structural connectivity (ISC) were extracted using common orthogonal basis extraction (COBE). Subsequently, multimodal canonical correlation analysis combined with joint independent component analysis (mCCA + jICA) was conducted to fusion analysis to identify the joint and unique independent components (ICs) across multiple modes. These ICs were utilized to generate features, and a support vector machine (SVM) model was implemented for the classification of MDD. The differences in individualized connectivity between patients and controls were compared using two-sample t test, with a significance threshold set at P < 0.05. The established model was tested and evaluated using the receiver operating characteristic (ROC) curve. The classification performance of the constructed individualized connectivity feature model after multisequence fusion increased from 72.2% to 90.3%. Furthermore, the prediction model showed significant predictive power for assessing the severity of depression in patients with MDD (r = 0.544). The integration of IFC and ISC through multisequence fusion enhances our capacity to identify MDD, highlighting the advantages of the individualized approach and underscoring its significance in MDD research. 1 TECHNICAL EFFICACY: Stage 2.

Changes in functional and structural brain connectivity following bilateral hand transplantation

As a surgical treatment following amputation or loss of an upper limb, nearly 200 hand transplantations have been completed to date. We report here a magnetic resonance imaging (MRI) investigation of functional and structural brain connectivity for a bilateral hand transplant patient (female, 60 years of age), with a preoperative baseline and three postoperative testing sessions each separated by approximately six months. We used graph theoretical analyses to estimate connectivity within and between modules (networks of anatomical nodes), particularly a sensorimotor network (SMN), from resting-state functional MRI and structural diffusion-weighted imaging (DWI). For comparison, corresponding MRI measures of connectivity were obtained from 10 healthy, age-matched controls, at a single testing session. The patient's within-module functional connectivity (both SMN and non-SMN modules), and structural within-SMN connectivity, were higher preoperatively than that of the controls, indicating a response to amputation. Postoperatively, the patient's within-module functional connectivity decreased towards the control participants' values, across the 1.5 years postoperatively, particularly for hand-related nodes within the SMN module, suggesting a return to a more canonical functional organization. Whereas the patient's structural connectivity values remained relatively constant postoperatively, some evidence suggested that structural connectivity supported the postoperative changes in within-module functional connectivity.

Involvement of the cerebellum in structural connectivity enhancement in episodic migraine

BackgroundThe pathophysiology of migraine remains poorly understood, yet a growing number of studies have shown structural connectivity disruptions across large-scale brain networks. Although both structural and functional changes have been found in the cerebellum of migraine patients, the cerebellum has barely been assessed in previous structural connectivity studies of migraine. Our objective is to investigate the structural connectivity of the entire brain, including the cerebellum, in individuals diagnosed with episodic migraine without aura during the interictal phase, compared with healthy controls.MethodsTo that end, 14 migraine patients and 15 healthy controls were recruited (all female), and diffusion-weighted and T1-weighted MRI data were acquired. The structural connectome was estimated for each participant based on two different whole-brain parcellations, including cortical and subcortical regions as well as the cerebellum. The structural connectivity patterns, as well as global and local graph theory metrics, were compared between patients and controls, for each of the two parcellations, using network-based statistics and a generalized linear model (GLM), respectively. We also compared the number of connectome streamlines within specific white matter tracts using a GLM.ResultsWe found increased structural connectivity in migraine patients relative to healthy controls with a distinct involvement of cerebellar regions, using both parcellations. Specifically, the node degree of the posterior lobe of the cerebellum was greater in patients than in controls and patients presented a higher number of streamlines within the anterior limb of the internal capsule. Moreover, the connectomes of patients exhibited greater global efficiency and shorter characteristic path length, which correlated with the age onset of migraine.ConclusionsA distinctive pattern of heightened structural connectivity and enhanced global efficiency in migraine patients compared to controls was identified, which distinctively involves the cerebellum. These findings provide evidence for increased integration within structural brain networks in migraine and underscore the significance of the cerebellum in migraine pathophysiology.

Similarity and characterization of structural and functional neural connections within species under isoflurane anesthesia in the common marmoset

The common marmoset is an essential model for understanding social cognition and neurodegenerative diseases. This study explored the structural and functional brain connectivity in a marmoset under isoflurane anesthesia, aiming to statistically overcome the effects of high inter-individual variability and noise-related confounds such as physiological noise, ensuring robust and reliable data. Similarities and differences in individual subject data, including assessments of functional and structural brain connectivities derived from resting-state functional MRI and diffusion tensor imaging were meticulously captured. The findings highlighted the high consistency of structural neural connections within the species, indicating a stable neural architecture, while functional connectivity under anesthesia displayed considerable variability. Through independent component and dual regression analyses, several distinct brain connectivities were identified, elucidating their characteristics under anesthesia. Insights into the structural and functional features of the marmoset brain from this study affirm its value as a neuroscience research model, promising advancements in the field through fundamental and translational studies.