Tau protein spreads through functionally connected neurons in Alzheimer's disease: a combined MEG/PET study.

Recent studies on Alzheimer's disease (AD) suggest that tau proteins spread through the brain following neuronal connections. Several mechanisms could be involved in this process: spreading between brain regions that interact strongly (functional connectivity); through the pattern of anatomical connections (structural connectivity); or simple diffusion. Using magnetoencephalography (MEG), we investigated which spreading pathways influence tau protein spreading by modelling the tau propagation process using an epidemic spreading model. We compared the modelled tau depositions with 18F-flortaucipir PET binding potentials at several stages of the AD continuum. In this cross-sectional study, we analysed source-reconstructed MEG data and dynamic 100-min 18F-flortaucipir PET from 57 subjects positive for amyloid-ß pathology [preclinical AD (n = 16), mild cognitive impairment (MCI) due to AD (n = 16) and AD dementia (n = 25)]. Cognitively healthy subjects without amyloid-ß pathology were included as controls (n = 25). Tau propagation was modelled as an epidemic process (susceptible-infected model) on MEG-based functional networks [in alpha (8-13 Hz) and beta (13-30 Hz) bands], a structural or diffusion network, starting from the middle and inferior temporal lobe. The group-level network of the control group was used as input for the model to predict tau deposition in three stages of the AD continuum. To assess performance, model output was compared to the group-specific tau deposition patterns as measured with 18F-flortaucipir PET. We repeated the analysis by using networks of the preceding disease stage and/or using regions with most observed tau deposition during the preceding stage as seeds. In the preclinical AD stage, the functional networks predicted most of the modelled tau-PET binding potential, with best correlations between model and tau-PET [corrected amplitude envelope correlation (AEC-c) alpha C = 0.584; AEC-c beta C = 0.569], followed by the structural network (C = 0.451) and simple diffusion (C = 0.451). Prediction accuracy declined for the MCI and AD dementia stages, although the correlation between modelled tau and tau-PET binding remained highest for the functional networks (C = 0.384; C = 0.376). Replacing the control-network with the network from the preceding disease stage and/or alternative seeds improved prediction accuracy in MCI but not in the dementia stage. These results suggest that in addition to structural connections, functional connections play an important role in tau spread, and highlight that neuronal dynamics play a key role in promoting this pathological process. Aberrant neuronal communication patterns should be taken into account when identifying targets for future therapy. Our results also suggest that this process is more important in earlier disease stages (preclinical AD/MCI); possibly, in later stages, other processes may be influential.

Surgical treatment options for spasticity in children and adolescents with hereditary spastic paraplegia.

PURPOSE: To provide an overview of outcome and complications of selective dorsal rhizotomy (SDR) and intrathecal baclofen pump implantation (ITB) for spasticity treatment in children with hereditary spastic paraplegia (HSP). METHODS: Retrospective study including children with HSP and SDR or ITB. Gross motor function measure (GMFM-66) scores and level of spasticity were assessed. RESULTS: Ten patients were included (most had mutations in ATL1 (n = 4) or SPAST (n = 3) genes). Four walked without and two with walking aids, four were non-walking children. Six patients underwent SDR, three patients ITB, and one both. Mean age at surgery was 8.9 ± 4.5 years with a mean follow-up of 3.4 ± 2.2 years. Five of the SDR patients were walking. Postoperatively spasticity in the legs was reduced in all patients. The change in GMFM-66 score was + 8.0 (0-19.7 min-max). The three ITB patients treated (SPAST (n = 2) and PNPLA6 (n = 1) gene mutation) were children with a progressive disease course. No complications of surgery occurred. CONCLUSIONS: SDR is a feasible treatment option in carefully selected children with HSP, especially in walking patients. The majority of patients benefit with respect to gross motor function, complication risk is low. ITB was used in children with severe and progressive disease.

Risk factors for neurophysiological events related to intraoperative halo-femoral traction in spinal deformity surgery.

PURPOSE: This study identifies risk factors for neurophysiological events caused by intraoperative halo-femoral traction (IOHFT) in patients with adolescent idiopathic scoliosis (AIS), and neuromuscular scoliosis (NMS). METHODS: Neurophysiological integrity was monitored using motor evoked potentials (MEPs). IONM event was defined as a decreased MEP amplitude of more than 80% of baseline in, at least, one muscle. Time between application of IOHFT and event, affected muscles, surgical stage, and time between removal of IOHFT and recovery of MEPs were described. Characteristics (age, height, weight, diagnosis, Cobb angle, and flexibility of the curve) of patients with and without IOHFT-events were compared using analysis of variance. Binary logistic regression analyses were performed to identify predictors. RESULTS: The study included 81 patients (age 15.6 ± 2.4 years, 53 females, AIS: n = 47, NMS n = 34). IOHFT-events occurred in 11 patients (13%; AIS n = 4, NMS n = 7). IOHFTevents affecting all limbs occurred pre-incision in NMS. Events affecting only the legs occurred during all stages of surgery. Patients with IOHFT-events were smaller (p = 0.009) and had stiffer curves (p = 0.046). Height was a predictor (odds ratio, 0.941; 95% confidence interval = 0.896-0.988). All MEPs recovered after removing IOHFT. CONCLUSION: Neurophysiologic events due to IOHFT were common, with the majority in patients with NMS. A shorter stature was a risk factor, and larger Cobb angle and stiffer curve were associated with IOHFT-events. Events occurred at any stage of surgery and involved upper and lower limbs. With an adequate response on IOHFT events, none of the patients had postoperative neurological impairments due to IOHFT.

Direction of information flow in large-scale resting-state networks is frequency-dependent.

Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-to-posterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequency-dependent reentry loops that are dominated by flow from parieto-occipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.

Low normal cerebrospinal fluid Aß42 levels predict clinical progression in nondemented subjects.

We studied whether continuous lower normal cerebrospinal fluid (CSF) amyloid ß1-42 (≥640pg/ml) levels were related with rate of clinical progression in a sample of 393 nondemented memory clinic patients. Lower normal levels were associated with faster clinical progression, and this depended on baseline cognitive status (subjective cognitive decline: hazard ratio [HR] = 0.57, p < 0.05; mild cognitive impairment: HR = 0.19, p < .01), indicating that normal CSF amyloid levels do not exclude incident Alzheimer disease. These findings suggest that research on preclinical markers for Alzheimer disease should take the continuum of CSF amyloid ß1-42 levels within the normal range into account. Ann Neurol 2017;81:749-753.

Altering neuronal excitability to preserve network connectivity in a computational model of Alzheimer's disease.

Neuronal hyperactivity and hyperexcitability of the cerebral cortex and hippocampal region is an increasingly observed phenomenon in preclinical Alzheimer's disease (AD). In later stages, oscillatory slowing and loss of functional connectivity are ubiquitous. Recent evidence suggests that neuronal dynamics have a prominent role in AD pathophysiology, making it a potentially interesting therapeutic target. However, although neuronal activity can be manipulated by various (non-)pharmacological means, intervening in a highly integrated system that depends on complex dynamics can produce counterintuitive and adverse effects. Computational dynamic network modeling may serve as a virtual test ground for developing effective interventions. To explore this approach, a previously introduced large-scale neural mass network with human brain topology was used to simulate the temporal evolution of AD-like, activity-dependent network degeneration. In addition, six defense strategies that either enhanced or diminished neuronal excitability were tested against the degeneration process, targeting excitatory and inhibitory neurons combined or separately. Outcome measures described oscillatory, connectivity and topological features of the damaged networks. Over time, the various interventions produced diverse large-scale network effects. Contrary to our hypothesis, the most successful strategy was a selective stimulation of all excitatory neurons in the network; it substantially prolonged the preservation of network integrity. The results of this study imply that functional network damage due to pathological neuronal activity can be opposed by targeted adjustment of neuronal excitability levels. The present approach may help to explore therapeutic effects aimed at preserving or restoring neuronal network integrity and contribute to better-informed intervention choices in future clinical trials in AD.

Selective impairment of hippocampus and posterior hub areas in Alzheimer's disease: an MEG-based multiplex network study.

Although frequency-specific network analyses have shown that functional brain networks are altered in patients with Alzheimer's disease, the relationships between these frequency-specific network alterations remain largely unknown. Multiplex network analysis is a novel network approach to study complex systems consisting of subsystems with different types of connectivity patterns. In this study, we used magnetoencephalography to integrate five frequency-band specific brain networks in a multiplex framework. Previous structural and functional brain network studies have consistently shown that hub brain areas are selectively disrupted in Alzheimer's disease. Accordingly, we hypothesized that hub regions in the multiplex brain networks are selectively targeted in patients with Alzheimer's disease in comparison to healthy control subjects. Eyes-closed resting-state magnetoencephalography recordings from 27 patients with Alzheimer's disease (60.6 ± 5.4 years, 12 females) and 26 controls (61.8 ± 5.5 years, 14 females) were projected onto atlas-based regions of interest using beamforming. Subsequently, source-space time series for both 78 cortical and 12 subcortical regions were reconstructed in five frequency bands (delta, theta, alpha 1, alpha 2 and beta band). Multiplex brain networks were constructed by integrating frequency-specific magnetoencephalography networks. Functional connections between all pairs of regions of interests were quantified using a phase-based coupling metric, the phase lag index. Several multiplex hub and heterogeneity metrics were computed to capture both overall importance of each brain area and heterogeneity of the connectivity patterns across frequency-specific layers. Different nodal centrality metrics showed consistently that several hub regions, particularly left hippocampus, posterior parts of the default mode network and occipital regions, were vulnerable in patients with Alzheimer's disease compared to control subjects. Of note, these detected vulnerable hubs in Alzheimer's disease were absent in each individual frequency-specific network, thus showing the value of integrating the networks. The connectivity patterns of these vulnerable hub regions in the patients were heterogeneously distributed across layers. Perturbed cognitive function and abnormal cerebrospinal fluid amyloid-ß42 levels correlated positively with the vulnerability of the hub regions in patients with Alzheimer's disease. Our analysis therefore demonstrates that the magnetoencephalography-based multiplex brain networks contain important information that cannot be revealed by frequency-specific brain networks. Furthermore, this indicates that functional networks obtained in different frequency bands do not act as independent entities. Overall, our multiplex network study provides an effective framework to integrate the frequency-specific networks with different frequency patterns and reveal neuropathological mechanism of hub disruption in Alzheimer's disease.

Horizontal visibility graph transfer entropy (HVG-TE): A novel metric to characterize directed connectivity in large-scale brain networks.

We propose a new measure, horizontal visibility graph transfer entropy (HVG-TE), to estimate the direction of information flow between pairs of time series. HVG-TE quantifies the transfer entropy between the degree sequences of horizontal visibility graphs derived from original time series. Twenty-one Rössler attractors unidirectionally coupled in the posterior-to-anterior direction were used to simulate 21-channel Electroencephalography (EEG) brain networks and validate the performance of the HVG-TE. We showed that the HVG-TE is robust to different levels of coupling strengths between the coupled Rössler attractors, a wide range of time delays, different sample sizes, the effects of noise and linear mixing, and the choice of reference for EEG data. We also applied HVG-TE to EEG data in 20 healthy controls and compared its performance to a recently introduces phase-based TE measure (PTE). We found that compared with PTE, HVG-TE consistently detected stronger posterior-to-anterior information flow patterns in the alpha-band (8-13Hz) EEG brain networks for three different references. Moreover, in contrast to PTE, HVG-TE does not require an assumption on the periodicity of input signals, therefore it can be more widely applicable, even for non-periodic signals. This study shows that the HVG-TE is a directed connectivity measure to characterise the direction of information flow in large-scale brain networks.

Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria.

During the past decade, a conceptual shift occurred in the field of Alzheimer's disease (AD) considering the disease as a continuum. Thanks to evolving biomarker research and substantial discoveries, it is now possible to identify the disease even at the preclinical stage before the occurrence of the first clinical symptoms. This preclinical stage of AD has become a major research focus as the field postulates that early intervention may offer the best chance of therapeutic success. To date, very little evidence is established on this "silent" stage of the disease. A clarification is needed about the definitions and lexicon, the limits, the natural history, the markers of progression, and the ethical consequence of detecting the disease at this asymptomatic stage. This article is aimed at addressing all the different issues by providing for each of them an updated review of the literature and evidence, with practical recommendations.

Diffusion changes predict cognitive and functional outcome: the LADIS study.

OBJECTIVE: A study was undertaken to determine whether diffusion-weighted imaging (DWI) abnormalities in normal-appearing brain tissue (NABT) and in white matter hyperintensities (WMH) predict longitudinal cognitive decline and disability in older individuals independently of the concomitant magnetic resonance imaging (MRI) findings. METHODS: A total of 340 LADIS (Leukoaraiosis and Disability Study) participants, aged 65 to 84 years, underwent brain MRI including DWI at baseline. Neuropsychological and functional assessments were carried out at study entry and repeated annually over a 3-year observational period. Linear mixed models and Cox regression survival analysis adjusted for demographics, WMH volume, lacunes, and brain atrophy were used to evaluate the independent effect of the DWI measures on change in cognitive performance and functional abilities. RESULTS: The mean global apparent diffusion coefficient (ADC) and the relative peak height and peak position of the ADC histogram in NABT predicted faster rate of decline in a composite score for speed and motor control. Higher mean ADC and lower peak height were also related to deterioration in executive functions and memory (specifically working memory), with peak height also being related to more rapid transition to disability and higher rate of mortality. Mean ADC in WMH had less pronounced effects on cognitive and functional outcomes. INTERPRETATION: DWI microstructural changes in NABT predict faster decline in psychomotor speed, executive functions, and working memory regardless of conventional MRI findings. Moreover, these changes are related to functional disability and higher mortality.

Local signal variability and functional connectivity: Sensitive measures of the excitation-inhibition ratio?

A novel network version of permutation entropy, the inverted joint permutation entropy (JPEinv), holds potential as non-invasive biomarker of abnormal excitation-inhibition (E-I) ratio in Alzheimer's disease (AD). In this computational modelling study, we test the hypotheses that this metric, and related measures of signal variability and functional connectivity, are sensitive to altered E-I ratios. The E-I ratio in each neural mass of a whole-brain computational network model was systematically varied. We evaluated whether JPEinv, local signal variability (by permutation entropy) and functional connectivity (by weighted symbolic mutual information (wsMI)) were related to E-I ratio, on whole-brain and regional level. The hub disruption index can identify regions primarily affected in terms of functional connectivity strength (or: degree) by the altered E-I ratios. Analyses were performed for a range of coupling strengths, filter and time-delay settings. On whole-brain level, higher E-I ratios were associated with higher functional connectivity (by JPEinv and wsMI) and lower local signal variability. These relationships were nonlinear and depended on the coupling strength, filter and time-delay settings. On regional level, hub-like regions showed a selective decrease in functional degree (by JPEinv and wsMI) upon a lower E-I ratio, and non-hub-like regions showed a selective increase in degree upon a higher E-I ratio. These results suggest that abnormal functional connectivity and signal variability, as previously reported in patients across the AD continuum, can inform us about altered E-I ratios. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-023-10003-x.

Erratum: Network-level permutation entropy of resting-state MEG recordings: A novel biomarker for early-stage Alzheimer's disease?

[This corrects the article DOI: 10.1162/netn_a_00224.].

Longitudinal resting-state EEG in amyloid-positive patients along the Alzheimer's disease continuum: considerations for clinical trials.

BACKGROUND: To enable successful inclusion of electroencephalography (EEG) outcome measures in Alzheimer's disease (AD) clinical trials, we retrospectively mapped the progression of resting-state EEG measures over time in amyloid-positive patients with mild cognitive impairment (MCI) or dementia due to AD. METHODS: Resting-state 21-channel EEG was recorded in 148 amyloid-positive AD patients (MCI, n = 88; dementia due to AD, n = 60). Two or more EEG recordings were available for all subjects. We computed whole-brain and regional relative power (i.e., theta (4-8 Hz), alpha1 (8-10 Hz), alpha2 (10-13 Hz), beta (13-30 Hz)), peak frequency, signal variability (i.e., theta permutation entropy), and functional connectivity values (i.e., alpha and beta corrected amplitude envelope correlation, theta phase lag index, weighted symbolic mutual information, inverted joint permutation entropy). Whole-group linear mixed effects models were used to model the development of EEG measures over time. Group-wise analysis was performed to investigate potential differences in change trajectories between the MCI and dementia subgroups. Finally, we estimated the minimum sample size required to detect different treatment effects (i.e., 50% less deterioration, stabilization, or 50% improvement) on the development of EEG measures over time, in hypothetical clinical trials of 1- or 2-year duration. RESULTS: Whole-group analysis revealed significant regional and global oscillatory slowing over time (i.e., increased relative theta power, decreased beta power), with strongest effects for temporal and parieto-occipital regions. Disease severity at baseline influenced the EEG measures' rates of change, with fastest deterioration reported in MCI patients. Only AD dementia patients displayed a significant decrease of the parieto-occipital peak frequency and theta signal variability over time. We estimate that 2-year trials, focusing on amyloid-positive MCI patients, require 36 subjects per arm (2 arms, 1:1 randomization, 80% power) to detect a stabilizing treatment effect on temporal relative theta power. CONCLUSIONS: Resting-state EEG measures could facilitate early detection of treatment effects on neuronal function in AD patients. Their sensitivity depends on the region-of-interest and disease severity of the study population. Conventional spectral measures, particularly recorded from temporal regions, present sensitive AD treatment monitoring markers.

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.

Resting-state oscillations reveal disturbed excitation-inhibition ratio in Alzheimer's disease patients.

An early disruption of neuronal excitation-inhibition (E-I) balance in preclinical animal models of Alzheimer's disease (AD) has been frequently reported, but is difficult to measure directly and non-invasively in humans. Here, we examined known and novel neurophysiological measures sensitive to E-I in patients across the AD continuum. Resting-state magnetoencephalography (MEG) data of 86 amyloid-biomarker-confirmed subjects across the AD continuum (17 patients diagnosed with subjective cognitive decline, 18 with mild cognitive impairment (MCI) and 51 with dementia due to probable AD (AD dementia)), 46 healthy elderly and 20 young control subjects were reconstructed to source-space. E-I balance was investigated by detrended fluctuation analysis (DFA), a functional E/I (fE/I) algorithm, and the aperiodic exponent of the power spectrum. We found a disrupted E-I ratio in AD dementia patients specifically, by a lower DFA, and a shift towards higher excitation, by a higher fE/I and a lower aperiodic exponent. Healthy subjects showed lower fE/I ratios (< 1.0) than reported in previous literature, not explained by age or choice of an arbitrary threshold parameter, which warrants caution in interpretation of fE/I results. Correlation analyses showed that a lower DFA (E-I imbalance) and a lower aperiodic exponent (more excitation) was associated with a worse cognitive score in AD dementia patients. In contrast, a higher DFA in the hippocampi of MCI patients was associated with a worse cognitive score. This MEG-study showed E-I imbalance, likely due to increased excitation, in AD dementia, but not in early stage AD patients. To accurately determine the direction of shift in E-I balance, validations of the currently used markers and additional in vivo markers of E-I are required.

White matter lesion progression in LADIS: frequency, clinical effects, and sample size calculations.

BACKGROUND AND PURPOSE: White matter lesion (WML) progression has been advocated as a surrogate marker in intervention trials on cerebral small vessel disease. We assessed the rate of visually rated WML progression, studied correlations between lesion progression and cognition, and estimated sample sizes for clinical trials with pure WML progression vs combined WML progression-cognitive outcomes. METHODS: Those 394 participants of the Leukoaraiosis and Disability Study (LADIS) study with magnetic resonance imaging scanning at baseline and 3-year follow-up were analyzed. WML progression rating relied on the modified Rotterdam Progression Scale. The Vascular Dementia Assessment Scale global score and a composite score of specific executive function tests assessed longitudinal change in cognition. Sample size calculations were based on the assumption that treatment reduces WML progression by 1 grade on the Rotterdam Progression Scale. RESULTS: WML progression related to deterioration in cognitive functioning. This relationship was less pronounced in subjects with early confluent and confluent lesions. Consequently, studies in which the outcome is cognitive change resulting from treatment effects on lesion progression will need between 1809 subjects per treatment arm when using executive tests and up to 18 853 subjects when using the Vascular Dementia Assessment Scale score. Studies having WML progression as the sole outcome will need only 58 or 70 individuals per treatment arm. CONCLUSIONS: WML progression is an interesting outcome for proof-of-concept studies in cerebral small vessel disease. If cognitive outcome measures are added to protocols, then sample size estimates increase substantially. Our data support the use of an executive test battery rather than the Vascular Dementia Assessment Scale as the primary cognitive outcome measure.

Relationship between progression of brain white matter changes and late-life depression: 3-year results from the LADIS study.

BACKGROUND: Brain white matter changes (WMC) and depressive symptoms are linked, but the directionality of this association remains unclear. AIMS: To investigate the relationship between baseline and incident depression and progression of white matter changes. METHOD: In a longitudinal multicentre pan-European study (Leukoaraiosis and Disability in the elderly, LADIS), participants aged over 64 underwent baseline magnetic resonance imaging (MRI) and clinical assessments. Repeat scans were obtained at 3 years. Depressive outcomes were assessed in terms of depressive episodes and the Geriatric Depression Scale (GDS). Progression of WMC was measured using the modified Rotterdam Progression scale. RESULTS: Progression of WMC was significantly associated with incident depression during year 3 of the study (P = 0.002) and remained significant after controlling for transition to disability, baseline WMC and baseline history of depression. There was no significant association between progression of WMC and GDS score, and no significant relationship between progression of WMC and history of depression at baseline. CONCLUSIONS: Our results support the vascular depression hypothesis and implicate WMC as causal in the pathogenesis of late-life depression.

Low cerebrospinal fluid sulfatide predicts progression of white matter lesions: The LADIS study.

BACKGROUND/AIMS: Demyelination and axonal degeneration are the hallmarks of established white matter lesions (WML). The neurochemistry of ongoing WML is only partially known. We explored cerebrospinal fluid (CSF) substances as markers of brain tissue damage in relation to progression of WML rated on magnetic resonance imaging. METHODS: CSF from elderly individuals with WML was analyzed for amyloid markers, total τ, hyperphosphorylated τ, neurofilament protein light subunit, sulfatide and CSF/serum-albumin ratio. After 3 years, a follow-up magnetic resonance imaging was performed. Progression of WML was rated using the Rotterdam Progression Scale (RPS). RESULTS: 37 subjects (age 73.6 ± 4.6 years) were included. Subjects with more pronounced progression (RPS > 2; n = 15) had lower mean sulfatide concentration at baseline as compared to subjects with no or minimal progression (RPS 0-2; n = 22) according to univariate analyses (p = 0.009). Sulfatide was the only biomarker that predicted the RPS score according to regression analysis, explaining 18.9% of the total variance (r = 0.38, p = 0.015). CONCLUSION: The correlation of CSF sulfatide levels and RPS scores may reflect a remyelination response to the demyelination process associated with WML. Furthermore, the results strengthen the notion that WML pathology is different from that of Alzheimer's disease.

Sensitive and reproducible MEG resting-state metrics of functional connectivity in Alzheimer's disease.

BACKGROUND: Analysis of functional brain networks in Alzheimer's disease (AD) has been hampered by a lack of reproducible, yet valid metrics of functional connectivity (FC). This study aimed to assess both the sensitivity and reproducibility of the corrected amplitude envelope correlation (AEC-c) and phase lag index (PLI), two metrics of FC that are insensitive to the effects of volume conduction and field spread, in two separate cohorts of patients with dementia due to AD versus healthy elderly controls. METHODS: Subjects with a clinical diagnosis of AD dementia with biomarker proof, and a control group of subjective cognitive decline (SCD), underwent two 5-min resting-state MEG recordings. Data consisted of a test (AD = 28; SCD = 29) and validation (AD = 29; SCD = 27) cohort. Time-series were estimated for 90 regions of interest (ROIs) in the automated anatomical labelling (AAL) atlas. For each of five canonical frequency bands, the AEC-c and PLI were calculated between all 90 ROIs, and connections were averaged per ROI. General linear models were constructed to compare the global FC differences between the groups, assess the reproducibility, and evaluate the effects of age and relative power. Reproducibility of the regional FC differences was assessed using the Mann-Whitney U tests, with correction for multiple testing using the false discovery rate (FDR). RESULTS: The AEC-c showed significantly and reproducibly lower global FC for the AD group compared to SCD, in the alpha (8-13 Hz) and beta (13-30 Hz) bands, while the PLI revealed reproducibly lower FC for the AD group in the delta (0.5-4 Hz) band and higher FC for the theta (4-8 Hz) band. Regionally, the beta band AEC-c showed reproducibility for almost all ROIs (except for 13 ROIs in the frontal and temporal lobes). For the other bands, the AEC-c and PLI did not show regional reproducibility after FDR correction. The theta band PLI was susceptible to the effect of relative power. CONCLUSION: For MEG, the AEC-c is a sensitive and reproducible metric, able to distinguish FC differences between patients with AD dementia and cognitively healthy controls. These two measures likely reflect different aspects of neural activity and show differential sensitivity to changes in neural dynamics.

Generating diagnostic profiles of cognitive decline and dementia using magnetoencephalography.

Accurate identification of the underlying cause(s) of cognitive decline and dementia is challenging due to significant symptomatic overlap between subtypes. This study presents a multi-class classification framework for subjects with subjective cognitive decline, mild cognitive impairment, Alzheimer's disease, dementia with Lewy bodies, fronto-temporal dementia and cognitive decline due to psychiatric illness, trained on source-localized resting-state magnetoencephalography data. Diagnostic profiles, describing probability estimates for each of the 6 diagnoses, were assigned to individual subjects. A balanced accuracy rate of 41% and multi-class area under the curve value of 0.75 were obtained for 6-class classification. Classification primarily depended on posterior relative delta, theta and beta power and amplitude-based functional connectivity in the beta and gamma frequency band. Dementia with Lewy bodies (sensitivity: 100%, precision: 20%) and Alzheimer's disease subjects (sensitivity: 51%, precision: 90%) could be classified most accurately. Fronto-temporal dementia subjects (sensitivity: 11%, precision: 3%) were most frequently misclassified. Magnetoencephalography biomarkers hold promise to increase diagnostic accuracy in a noninvasive manner. Diagnostic profiles could provide an intuitive tool to clinicians and may facilitate implementation of the classifier in the memory clinic.