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Local network connectivity disruptions in Alzheimer's disease patients have been found using graph analysis in BOLD fMRI. Other studies using MEG and cortical thickness measures, however, show more global long distance connectivity changes, both in functional and structural imaging data. The form and role of functional connectivity changes thus remains ambiguous. The current study shows more conclusive data on connectivity changes in early AD using graph analysis on resting-state condition fMRI data. 18 mild AD patients and 21 healthy age-matched control subjects without memory complaints were investigated in resting-state condition with MRI at 1.5 Tesla. Functional coupling between brain regions was calculated on the basis of pair-wise synchronizations between regional time-series. Local (cluster coefficient) and global (path length) network measures were quantitatively defined. Compared to controls, the characteristic path length of AD functional networks is closer to the theoretical values of random networks, while no significant differences were found in cluster coefficient. The whole-brain average synchronization does not differ between Alzheimer and healthy control groups. Post-hoc analysis of the regional synchronization reveals increased AD synchronization involving the frontal cortices and generalized decreases located at the parietal and occipital regions. This effectively translates in a global reduction of functional long-distance links between frontal and caudal brain regions. We present evidence of AD-induced changes in global brain functional connectivity specifically affecting long-distance connectivity. This finding is highly relevant for it supports the anterior-posterior disconnection theory and its role in AD. Our results can be interpreted as reflecting the randomization of the brain functional networks in AD, further suggesting a loss of global information integration in disease.

Functional brain networks are shaped and constrained by the underlying structural network. However, functional networks are not merely a one-to-one reflection of the structural network. Several theories have been put forward to understand the relationship between structural and functional networks. However, it remains unclear how these theories can be unified. Two existing recent theories state that 1) functional networks can be explained by all possible walks in the structural network, which we will refer to as the series expansion approach, and 2) functional networks can be explained by a weighted combination of the eigenmodes of the structural network, the so-called eigenmode approach. To elucidate the unique or common explanatory power of these approaches to estimate functional networks from the structural network, we analysed the relationship between these two existing views. Using linear algebra, we first show that the eigenmode approach can be written in terms of the series expansion approach, i.e., walks on the structural network associated with different hop counts correspond to different weightings of the eigenvectors of this network. Second, we provide explicit expressions for the coefficients for both the eigenmode and series expansion approach. These theoretical results were verified by empirical data from Diffusion Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI), demonstrating a strong correlation between the mappings based on both approaches. Third, we analytically and empirically demonstrate that the fit of the eigenmode approach to measured functional data is always at least as good as the fit of the series expansion approach, and that errors in the structural data lead to large errors of the estimated coefficients for the series expansion approach. Therefore, we argue that the eigenmode approach should be preferred over the series expansion approach. Results hold for eigenmodes of the weighted adjacency matrices as well as eigenmodes of the graph Laplacian. ​Taken together, these results provide an important step towards unification of existing theories regarding the structure-function relationships in brain networks. •Two prominent theories on mappings between structural and functional networks are:•Functional networks can be explained by all possible walks in the structural network.•Functional networks can be explained by the eigenmodes of the structural network.•We show that these two approaches are equivalent using empirical and simulated data.•We provide explicit expressions for model coefficients for both approaches.

Background An imbalance of adipokines, hormones secreted by white adipose tissue, is suggested to play a role in the immunopathology of multiple sclerosis (MS). In people with MS (PwMS) of the same age, we aimed to determine whether the adipokines adiponectin, leptin, and resistin are associated with MS disease severity. Furthermore, we aimed to investigate whether these adipokines mediate the association between body mass index (BMI) and MS disease severity. Methods Adiponectin, resistin, and leptin were determined in serum using ELISA. 288 PwMS and 125 healthy controls (HC) were included from the Project Y cohort, a population-based cross-sectional study of people with MS born in the Netherlands in 1966, and age and sex-matched HC. Adipokine levels and BMI were related to demographic, clinical and disability measures, and MRI-based brain volumes. Results Adiponectin levels were 1.2 fold higher in PwMS vs. HC, especially in secondary progressive MS. Furthermore, we found a sex-specific increase in adiponectin levels in primary progressive (PP) male patients compared to male controls. Leptin and resistin levels did not differ between PwMS and HC, however, leptin levels were associated with higher disability (EDSS) and resistin strongly related to brain volumes in progressive patients, especially in several grey matter regions in PPMS. Importantly, correction for BMI did not significantly change the results. Conclusion In PwMS of the same age, we found associations between adipokines (adiponectin, leptin, and resistin) and a range of clinical and radiological metrics. These associations were independent of BMI, indicating distinct mechanisms.

The brain connectome of multiple sclerosis (MS) has been investigated by several previous studies; however, it is still unknown how the network changes in clinically isolated syndrome (CIS), the earliest stage of MS, and how network alterations on a functional level relate to the structural level in MS disease. Here, we investigated the topological alterations of both the structural and functional connectomes in 41 CIS and 32 MS patients, compared to 35 healthy controls, by combining diffusion tensor imaging and resting-state functional MRI with graph analysis approaches. We found that the structural connectome showed a deviation from the optimal pattern as early as the CIS stage, while the functional connectome only showed local changes in MS patients, not in CIS. When comparing two patient groups, the changes appear more severe in MS. Importantly, the disruptions of structural and functional connectomes in patients occurred in the same direction and locally correlated in sensorimotor component. Finally, the extent of structural network changes was correlated with several clinical variables in MS patients. Together, the results suggested early disruption of the structural brain connectome in CIS patients and provided a new perspective for investigating the relationship of the structural and functional alterations in MS.

Studies disagree on the location of grey matter (GM) atrophy in the multiple sclerosis (MS) brain. To examine the consistency between FSL, FreeSurfer, SPM for GM atrophy measurement (for volumes, patient/control discrimination, and correlations with cognition). 127 MS patients and 50 controls were included and cortical and deep grey matter (DGM) volumetrics were performed. Consistency of volumes was assessed with Intraclass Correlation Coefficient/ICC. Consistency of patients/controls discrimination was assessed with Cohen's d, t-tests, MANOVA and a penalized double-loop logistic classifier. Consistency of association with cognition was assessed with Pearson correlation coefficient and ANOVA. Voxel-based morphometry (SPM-VBM and FSL-VBM) and vertex-wise FreeSurfer were used for group-level comparisons. The highest volumetry ICC were between SPM and FreeSurfer for cortical regions, and the lowest between SPM and FreeSurfer for DGM. The caudate nucleus and temporal lobes had high consistency between all software, while amygdala had lowest volumetric consistency. Consistency of patients/controls discrimination was largest in the DGM for all software, especially for thalamus and pallidum. The penalized double-loop logistic classifier most often selected the thalamus, pallidum and amygdala for all software. FSL yielded the largest number of significant correlations. DGM yielded stronger correlations with cognition than cortical volumes. Bilateral putamen and left insula volumes correlated with cognition using all methods. GM volumes from FreeSurfer, FSL and SPM are different, especially for cortical regions. While group-level separation between MS and controls is comparable, correlations between regional GM volumes and clinical/cognitive variables in MS should be cautiously interpreted.

In this study we segment the hippocampus according to functional connectivity assessed from resting state functional magnetic resonance images in healthy subjects and in patients with Alzheimer's disease (AD). We recorded the resting FMRI signal from 16 patients and 22 controls. We used seed-based functional correlation analyses to calculate partial correlations of all voxels in the hippocampus relative to characteristic regional signal changes in the thalamus, the prefrontal cortex (PFC) and the posterior cingulate cortex (PCC), while controlling for ventricular CSF and white matter signals. Group comparisons were carried out controlling for age, gender, hippocampal volume and brain volume. The strength of functional connectivity in each region also was correlated with neuropsychological measures. We found that the hippocampus can be segmented into three distinct functional subregions (head, body, and tail), according to the relative connectivity with PFC, PCC and thalamus, respectively. The AD group showed stronger hippocampus–PFC and weaker hippocampus–PCC functional connectivity, the magnitudes of which correlated with MMSE in both cases. The results are consistent with an adaptive role of the PFC in the context of progression of dysfunction in PCC during earlier stages of AD. Extension of our approach could integrate regional volume measures for the hippocampus with their functional connectivity patterns in ways that should increase sensitivity for assessment of AD onset and progression. ► Human hippocampus can be divided into 3 regions, based on functional connectivity. ► Hippocampal functional connectivity changes in Alzheimer's disease. ► Changes in the functional connectivity correlates with the episodic memory.

The relation between pathological findings and clinical and cognitive decline in Multiple Sclerosis remains unclear. Here, we tested the hypothesis that altered functional connectivity could provide a missing link between structural findings, such as thalamic atrophy and white matter lesion load, and clinical and cognitive dysfunction. Resting-state magnetoencephalography recordings from 21 MS patients and 17 gender- and age matched controls were projected onto atlas-based regions-of-interest using beamforming. Average functional connectivity was computed for each ROI and literature-based resting-state networks using the phase-lag index. Structural measures of whole brain and thalamic atrophy and lesion load were estimated from MRI scans. Global analyses showed lower functional connectivity in the alpha2 band and higher functional connectivity in the beta band in patients with Multiple Sclerosis. Additionally, alpha2 band functional connectivity was lower for the patients in two resting-state networks, namely the default mode network and the visual network. Higher beta band functional connectivity was found in the default mode network and in the temporo-parietal network. Lower alpha2 band functional connectivity in the visual network was related to lower thalamic volumes. Beta band functional connectivity correlated positively with disability scores, most prominently in the default mode network, and correlated negatively with cognitive performance in this network. These findings illustrate the relationship between thalamic atrophy, altered functional connectivity and clinical and cognitive dysfunction in MS, which could serve as a bridge to understand how neurodegeneration is associated with altered functional connectivity and subsequently clinical and cognitive decline.

To reveal possible differences in whole brain topology of epileptic glioma patients, being low-grade glioma (LGG) and high-grade glioma (HGG) patients. We studied functional networks in these patients and compared them to those in epilepsy patients with non-glial lesions (NGL) and healthy controls. Finally, we related network characteristics to seizure frequency and cognitive performance within patient groups. We constructed functional networks from pre-surgical resting-state magnetoencephalography (MEG) recordings of 13 LGG patients, 12 HGG patients, 10 NGL patients, and 36 healthy controls. Normalized clustering coefficient and average shortest path length as well as modular structure and network synchronizability were computed for each group. Cognitive performance was assessed in a subset of 11 LGG and 10 HGG patients. LGG patients showed decreased network synchronizability and decreased global integration compared to healthy controls in the theta frequency range (4-8 Hz), similar to NGL patients. HGG patients' networks did not significantly differ from those in controls. Network characteristics correlated with clinical presentation regarding seizure frequency in LGG patients, and with poorer cognitive performance in both LGG and HGG glioma patients. Lesion histology partly determines differences in functional networks in glioma patients suffering from epilepsy. We suggest that differences between LGG and HGG patients' networks are explained by differences in plasticity, guided by the particular lesional growth pattern. Interestingly, decreased synchronizability and decreased global integration in the theta band seem to make LGG and NGL patients more prone to the occurrence of seizures and cognitive decline.

To evaluate the intrinsic and extrinsic microstructural factors contributing to atrophy within individual thalamic subregions in multiple sclerosis using in vivo high-gradient diffusion MRI. In this cross-sectional study, 41 people with multiple sclerosis and 34 age and sex-matched healthy controls underwent 3T MRI with up to 300 mT/m gradients using a multi-shell diffusion protocol consisting of eight b-values and diffusion time of 19 ms. Each thalamus was parcellated into 25 subregions for volume determination and diffusion metric estimation. The soma and neurite density imaging model was applied to obtain estimates of intra-neurite, intra-soma, and extra-cellular signal fractions for each subregion and within structurally connected white matter trajectories and cortex. Multiple sclerosis-related volume loss was more pronounced in posterior/medial subregions than anterior/ventral subregions. Intra-soma signal fraction was lower in multiple sclerosis, reflecting reduced cell body density, while the extra-cellular signal fraction was higher, reflecting greater extra-cellular space, both of which were observed more in posterior/medial subregions than anterior/ventral subregions. Lower intra-neurite signal fraction in connected normal-appearing white matter and lower intra-soma signal fraction of structurally connected cortex were associated with reduced subregional thalamic volumes. Intrinsic and extrinsic microstructural measures independently related to subregional volume with heterogeneity across atrophy-prone thalamic nuclei. Extrinsic microstructural alterations predicted left anteroventral, intrinsic microstructural alterations predicted bilateral medial pulvinar, and both intrinsic and extrinsic factors predicted lateral geniculate and medial mediodorsal volumes. Our results might be reflective of the involvement of anterograde and retrograde degeneration from white matter demyelination and cerebrospinal fluid-mediated damage in subregional thalamic volume loss.

Objective To quantify alterations in soma and neurite density imaging measures within and surrounding cortical lesions in people with multiple sclerosis using in vivo high‐gradient diffusion MRI. Methods In this cross‐sectional study, 41 people with multiple sclerosis and 34 age‐ and sex‐matched healthy controls underwent 3 T high‐gradient diffusion MRI. Cortical lesions were segmented on artificial intelligence‐enabled double inversion recovery images. “Inner” and “outer” perilesional layers were segmented as two expanding shells of 2 mm surrounding a cortical lesion. Intracellular, intra‐neurite, and extracellular signal fractions and apparent soma radius were estimated in (peri)lesional and normal‐appearing cortex. Results Cortical lesions were present in all people with multiple sclerosis with a median count of 8 [IQR 5–18] and total volume of 0.16 [0.09–0.46 mL]. People with multiple sclerosis (mean 0.27 ± 0.03) showed lower normalized cortical volumes compared to healthy controls (0.30 ± 0.02). Compared to healthy controls (mean 0.58 ± 0.028), normal‐appearing cortex in multiple sclerosis (0.57 ± 0.034) showed lower intra‐cellular signal fraction. Cortical lesions (0.49 ± 0.089) exhibited lower intra‐cellular signal fractions compared to perilesional (“inner”: 0.55 ± 0.049, “outer”: 0.55 ± 0.039) and normal‐appearing cortex, demonstrating a gradation of change. The soma radius varied significantly across cortices, becoming smaller when moving outward from cortical lesions (cortical lesions: 10.38 ± 0.209 μm, “inner” layer: 10.19 ± 0.140 μm, “outer” layer: 10.07 ± 0.149 μm, normal‐appearing cortex: 9.99 ± 0.127 μm). Interpretation Cortical cell body loss in multiple sclerosis is most pronounced in cortical lesions and also present in normal‐appearing cortex. Gradients of diffusion microstructural alterations moving outward from cortical lesions toward normal‐appearing cortex highlight the potential of high‐gradient diffusion MRI to identify both focal and diffuse cortical pathology.