Functional Neurophysiological Biomarkers of Early-Stage Alzheimer's Disease: A Perspective of Network Hyperexcitability in Disease Progression

Network Hyperexcitability in Early-Stage Alzheimer's Disease: Evaluation of Functional Connectivity Biomarkers in a Computational Disease Model

Background

There is increasing evidence from animal and clinical studies that network hyperexcitability (NH) may be an important pathophysiological process and potential target for treatment in early Alzheimer's disease (AD). Measures of functional connectivity (FC) have been proposed as promising biomarkers for NH, but it is unknown which measure has the highest sensitivity for early-stage changes in the excitation/inhibition balance.

Objective

We aim to test the performance of different FC measures in detecting NH at the earliest stage using a computational approach.

Methods

We use a whole brain computational model of activity dependent degeneration to simulate progressive AD pathology and NH. We investigate if and at what stage four measures of FC (amplitude envelope correlation corrected [AECc], phase lag index [PLI], joint permutation entropy [JPE] and a new measure: phase lag time [PLT]) can detect early-stage AD pathophysiology.

Results

The activity dependent degeneration model replicates spectral changes in line with clinical data and demonstrates increasing NH. Compared to relative theta power as a gold standard the AECc and PLI are shown to be less sensitive in detecting early-stage NH and AD-related neurophysiological abnormalities, while the JPE and the PLT show more sensitivity with excellent test characteristics.

Conclusions

Novel FC measures, which are better in detecting rapid fluctuations in neural activity and connectivity, may be superior to well-known measures such as the AECc and PLI in detecting early phase neurophysiological abnormalities and in particular NH in AD. These markers could improve early diagnosis and treatment target identification.

Neuronal connectivity, behavioral, and transcriptional alterations associated with the loss of MARK2

Neuronal connectivity is essential for adaptive brain responses and can be modulated by dendritic spine plasticity and the intrinsic excitability of individual neurons. Dysregulation of these processes can lead to aberrant neuronal activity, which has been associated with numerous neurological disorders including autism, epilepsy, and Alzheimer's disease. Nonetheless, the molecular mechanisms underlying aberrant neuronal connectivity remains unclear. We previously found that the serine/threonine kinase Microtubule Affinity Regulating Kinase 2 (MARK2), also known as Partitioning Defective 1b (Par1b), is important for the formation of dendritic spines in vitro. However, despite its genetic association with several neurological disorders, the in vivo impact of MARK2 on neuronal connectivity and cognitive functions remains unclear. Here, we demonstrate that loss of MARK2 in vivo results in changes to dendritic spine morphology, which in turn leads to a decrease in excitatory synaptic transmission. Additionally, loss of MARK2 produces substantial impairments in learning and memory, anxiety, and social behavior. Notably, MARK2 deficiency results in heightened seizure susceptibility. Consistent with this observation, RNAseq analysis reveals transcriptional changes in genes regulating synaptic transmission and ion homeostasis. These findings underscore the in vivo role of MARK2 in governing synaptic connectivity, cognitive functions, and seizure susceptibility.

Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias

Introduction

Neural circuit alterations lay at the core of brain physiopathology, and yet are hard to unveil in living subjects. The Virtual Brain (TVB) modeling, by exploiting structural and functional magnetic resonance imaging (MRI), yields mesoscopic parameters of connectivity and synaptic transmission.

Methods

We used TVB to simulate brain networks, which are key for human brain function, in Alzheimer's disease (AD) and frontotemporal dementia (FTD) patients, whose connectivity and synaptic parameters remain largely unknown; we then compared them to healthy controls, to reveal novel in vivo pathological hallmarks.

Results

The pattern of simulated parameter differed between AD and FTD, shedding light on disease-specific alterations in brain networks. Individual subjects displayed subtle differences in network parameter patterns that significantly correlated with their individual neuropsychological, clinical, and pharmacological profiles.

Discussion

These TVB simulations, by informing about a new personalized set of networks parameters, open new perspectives for understanding dementias mechanisms and design personalized therapeutic approaches.

Diagnosis and treatment of late-onset myoclonic epilepsy in Down syndrome (LOMEDS): A systematic review with individual patients' data analysis

INTRODUCTION

The late onset myoclonic epilepsy in Down Syndrome (LOMEDS) is a peculiar epilepsy type characterized by cortical myoclonus and generalized tonic-clonic seizures (GTCS), in people suffering from cognitive decline in Down syndrome (DS). In this review, we analyzed available data on the diagnostic and therapeutic management of individuals with LOMEDS.

METHODS

We performed a systematic search of the literature to identify the diagnostic and therapeutic management of patients with LOMEDS. The following databases were used: PubMed, Google Scholar, EMBASE, CrossRef. The protocol was registered on PROSPERO (registration code: CRD42023390748).

RESULTS

Data from 46 patients were included. DS was diagnosed according to the patient's clinical and genetic characteristics. Diagnosis of Alzheimer's dementia (AD) preceded the onset of epilepsy in all cases. Both myoclonic seizures (MS) and generalized tonic-clonic seizures (GTCS) were reported, the latter preceding the onset of MS in 28 cases. EEG was performed in 45 patients, showing diffuse theta/delta slowing with superimposed generalized spike-and-wave or polyspike-and-wave. A diffuse cortical atrophy was detected in 34 patients on neuroimaging. Twenty-seven patients were treated with antiseizure medication (ASM) monotherapy, with reduced seizure frequency in 17 patients. Levetiracetam and valproic acid were the most used ASMs. Up to 41% of patients were unresponsive to first-line treatment and needed adjunctive therapy for seizure control.

CONCLUSIONS

AD-related pathological changes in the brain may play a role in LOMEDS onset, although the mechanism underlying this phenomenon is still unknown. EEG remains the most relevant investigation to be performed. A significant percentage of patients developed a first-line ASM refractory epilepsy. ASMs which modulate the glutamatergic system may represent a good therapeutic option.

Network Hyperexcitability in Early Alzheimer's Disease: Is Functional Connectivity a Potential Biomarker?

Network hyperexcitability (NH) is an important feature of the pathophysiology of Alzheimer's disease. Functional connectivity (FC) of brain networks has been proposed as a potential biomarker for NH. Here we use a whole brain computational model and resting-state MEG recordings to investigate the relation between hyperexcitability and FC. Oscillatory brain activity was simulated with a Stuart Landau model on a network of 78 interconnected brain regions. FC was quantified with amplitude envelope correlation (AEC) and phase coherence (PC). MEG was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Functional connectivity was determined with the corrected AECc and phase lag index (PLI), in the 4-8 Hz and the 8-13 Hz bands. The excitation/inhibition balance in the model had a strong effect on both AEC and PC. This effect was different for AEC and PC, and was influenced by structural coupling strength and frequency band. Empirical FC matrices of SCD and MCI showed a good correlation with model FC for AEC, but less so for PC. For AEC the fit was best in the hyperexcitable range. We conclude that FC is sensitive to changes in E/I balance. The AEC was more sensitive than the PLI, and results were better for the thetaband than the alpha band. This conclusion was supported by fitting the model to empirical data. Our study justifies the use of functional connectivity measures as surrogate markers for E/I balance.

The fate of interneurons, GABAA receptor sub-types and perineuronal nets in Alzheimer's disease

Alzheimer's disease (AD) is the most common neurological disease, which is associated with gradual memory loss and correlated with synaptic hyperactivity and abnormal oscillatory rhythmic brain activity that precedes phenotypic alterations and is partly responsible for the spread of the disease pathology. Synaptic hyperactivity is thought to be because of alteration in the homeostasis of phasic and tonic synaptic inhibition, which is orchestrated by the GABA_A inhibitory system, encompassing subclasses of interneurons and GABA_A receptors, which play a vital role in cognitive functions, including learning and memory. Furthermore, the extracellular matrix, the perineuronal nets (PNNs) which often go unnoticed in considerations of AD pathology, encapsulate the inhibitory cells and neurites in critical brain regions and have recently come under the light for their crucial role in synaptic stabilisation and excitatory-inhibitory balance and when disrupted, serve as a potential trigger for AD-associated synaptic imbalance. Therefore, in this review, we summarise the current understanding of the selective vulnerability of distinct interneuron subtypes, their synaptic and extrasynaptic GABA_A R subtypes as well as the changes in PNNs in AD, detailing their contribution to the mechanisms of disease development. We aim to highlight how seemingly unique malfunction in each component of the interneuronal GABA inhibitory system can be tied together to result in critical circuit dysfunction, leading to the irreversible symptomatic damage observed in AD.

Inferring excitation-inhibition dynamics using a maximum entropy model unifying brain structure and function

Neural activity coordinated across different scales from neuronal circuits to large-scale brain networks gives rise to complex cognitive functions. Bridging the gap between micro- and macroscale processes, we present a novel framework based on the maximum entropy model to infer a hybrid resting-state structural connectome, representing functional interactions constrained by structural connectivity. We demonstrate that the structurally informed network outperforms the unconstrained model in simulating brain dynamics, wherein by constraining the inference model with the network structure we may improve the estimation of pairwise BOLD signal interactions. Further, we simulate brain network dynamics using Monte Carlo simulations with the new hybrid connectome to probe connectome-level differences in excitation-inhibition balance between apolipoprotein E (APOE)-ε4 carriers and noncarriers. Our results reveal sex differences among APOE-ε4 carriers in functional dynamics at criticality; specifically, female carriers appear to exhibit a lower tolerance to network disruptions resulting from increased excitatory interactions. In sum, the new multimodal network explored here enables analysis of brain dynamics through the integration of structure and function, providing insight into the complex interactions underlying neural activity such as the balance of excitation and inhibition.

Neurophysiological effects of human-derived pathological tau conformers in the APPKM670/671NL.PS1/L166P amyloid mouse model of Alzheimer's disease

Alzheimer’s Disease (AD) is a neurodegenerative disease characterized by two main pathological hallmarks: amyloid plaques and intracellular tau neurofibrillary tangles. However, a majority of studies focus on the individual pathologies and seldom on the interaction between the two pathologies. Herein, we present the longitudinal neuropathological and neurophysiological effects of a combined amyloid-tau model by hippocampal seeding of human-derived tau pathology in the APP.PS1/L166P amyloid animal model. We statistically assessed both neurophysiological and pathological changes using linear mixed modelling to determine if factors such as the age at which animals were seeded, genotype, seeding or buffer, brain region where pathology was quantified, and time-post injection differentially affect these outcomes. We report that AT8-positive tau pathology progressively develops and is facilitated by the amount of amyloid pathology present at the time of injection. The amount of AT8-positive tau pathology was influenced by the interaction of age at which the animal was injected, genotype, and time after injection. Baseline pathology-related power spectra and Higuchi Fractal Dimension (HFD) score alterations were noted in APP.PS1/L166P before any manipulations were performed, indicating a baseline difference associated with genotype. We also report immediate localized hippocampal dysfunction in the electroencephalography (EEG) power spectra associated with tau seeding which returned to comparable levels at 1 month-post-injection. Longitudinal effects of seeding indicated that tau-seeded wild-type mice showed an increase in gamma power earlier than buffer control comparisons which was influenced by the age at which the animal was injected. A reduction of hippocampal broadband power spectra was noted in tau-seeded wild-type mice, but absent in APP.PS1 animals. HFD scores appeared to detect subtle effects associated with tau seeding in APP.PS1 animals, which was differentially influenced by genotype. Notably, while tau histopathological changes were present, a lack of overt longitudinal electrophysiological alterations was noted, particularly in APP.PS1 animals that feature both pathologies after seeding, reiterating and underscoring the difficulty and complexity associated with elucidating physiologically relevant and translatable biomarkers of Alzheimer’s Disease at the early stages of the disease.