Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain

Recent Progress in Brain Network Models for Medical Applications: A Review

Importance: Pathological perturbations of the brain often spread via connectome to fundamentally alter functional consequences. By integrating multimodal neuroimaging data with mathematical neural mass modeling, brain network models (BNMs) enable to quantitatively characterize aberrant network dynamics underlying multiple neurological and psychiatric disorders. We delved into the advancements of BNM-based medical applications, discussed the prevalent challenges within this field, and provided possible solutions and future directions. Highlights: This paper reviewed the theoretical foundations and current medical applications of computational BNMs. Composed of neural mass models, the BNM framework allows to investigate large-scale brain dynamics behind brain diseases by linking the simulated functional signals to the empirical neurophysiological data, and has shown promise in exploring neuropathological mechanisms, elucidating therapeutic effects, and predicting disease outcome. Despite that several limitations existed, one promising trend of this research field is to precisely guide clinical neuromodulation treatment based on individual BNM simulation. Conclusion: BNM carries the potential to help understand the mechanism underlying how neuropathology affects brain network dynamics, further contributing to decision-making in clinical diagnosis and treatment. Several constraints must be addressed and surmounted to pave the way for its utilization in the clinic.

Biophysical models applied to dementia patients reveal links between geographical origin, gender, disease duration, and loss of neural inhibition

BACKGROUND

The hypothesis of decreased neural inhibition in dementia has been sparsely studied in functional magnetic resonance imaging (fMRI) data across patients with different dementia subtypes, and the role of social and demographic heterogeneities on this hypothesis remains to be addressed.

METHODS

We inferred regional inhibition by fitting a biophysical whole-brain model (dynamic mean field model with realistic inter-areal connectivity) to fMRI data from 414 participants, including patients with Alzheimer's disease, behavioral variant frontotemporal dementia, and controls. We then investigated the effect of disease condition, and demographic and clinical variables on the local inhibitory feedback, a variable related to the maintenance of balanced neural excitation/inhibition.

RESULTS

Decreased local inhibitory feedback was inferred from the biophysical modeling results in dementia patients, specific to brain areas presenting neurodegeneration. This loss of local inhibition correlated positively with years with disease, and showed differences regarding the gender and geographical origin of the patients. The model correctly reproduced known disease-related changes in functional connectivity.

CONCLUSIONS

Results suggest a critical link between abnormal neural and circuit-level excitability levels, the loss of grey matter observed in dementia, and the reorganization of functional connectivity, while highlighting the sensitivity of the underlying biophysical mechanism to demographic and clinical heterogeneities in the patient population.

A Multiscale Closed-Loop Neurotoxicity Model of Alzheimer's Disease Progression Explains Functional Connectivity Alterations

The accumulation of amyloid-β (Aβ) and hyperphosphorylated-tau (hp-tau) are two classical histopathological biomarkers in Alzheimer's disease (AD). However, their detailed interactions with the electrophysiological changes at the meso- and macroscale are not yet fully understood. We developed a mechanistic multiscale model of AD progression, linking proteinopathy to its effects on neural activity and vice-versa. We integrated a heterodimer model of prion-like protein propagation and a brain network model of Jansen-Rit neural masses derived from human neuroimaging data whose parameters varied due to neurotoxicity. Results showed that changes in inhibition guided the electrophysiological alterations found in AD, and these changes were mainly attributed to Aβ effects. Additionally, we found a causal disconnection between cellular hyperactivity and interregional hypersynchrony contrary to previous beliefs. Finally, we demonstrated that early Aβ and hp-tau depositions' location determine the spatiotemporal profile of the proteinopathy. The presented model combines the molecular effects of both Aβ and hp-tau together with a mechanistic protein propagation model and network effects within a closed-loop model. This holds the potential to enlighten the interplay between AD mechanisms on various scales, aiming to develop and test novel hypotheses on the contribution of different AD-related variables to the disease evolution.

Prioritization of genes for translation: a computational approach

INTRODUCTION

Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation.

AREAS COVERED

In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes.

EXPERT OPINION

Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.

Elucidating multifinal and equifinal pathways to developmental disorders by constructing real-world neurorobotic models

Vigorous research has been conducted to accumulate biological and theoretical knowledge about neurodevelopmental disorders, including molecular, neural, computational, and behavioral characteristics; however, these findings remain fragmentary and do not elucidate integrated mechanisms. An obstacle is the heterogeneity of developmental pathways causing clinical phenotypes. Additionally, in symptom formations, the primary causes and consequences of developmental learning processes are often indistinguishable. Herein, we review developmental neurorobotic experiments tackling problems related to the dynamic and complex properties of neurodevelopmental disorders. Specifically, we focus on neurorobotic models under predictive processing lens for the study of developmental disorders. By constructing neurorobotic models with predictive processing mechanisms of learning, perception, and action, we can simulate formations of integrated causal relationships among neurodynamical, computational, and behavioral characteristics in the robot agents while considering developmental learning processes. This framework has the potential to bind neurobiological hypotheses (excitation-inhibition imbalance and functional disconnection), computational accounts (unusual encoding of uncertainty), and clinical symptoms. Developmental neurorobotic approaches may serve as a complementary research framework for integrating fragmented knowledge and overcoming the heterogeneity of neurodevelopmental disorders.

Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings

INTRODUCTION

Early identification of Alzheimer's disease (AD) is necessary for a timely onset of therapeutic care. However, cortical structural alterations associated with AD are difficult to discern.

METHODS

We developed a cortical model of AD-related neurodegeneration accounting for slowing of local dynamics and global connectivity degradation. In a monocentric study we collected electroencephalography (EEG) recordings at rest from participants in healthy (HC, n = 17), subjective cognitive decline (SCD, n = 58), and mild cognitive impairment (MCI, n = 44) conditions. For each patient, we estimated neurodegeneration model parameters based on individual EEG recordings.

RESULTS

Our model outperformed standard EEG analysis not only in discriminating between HC and MCI conditions (F1 score 0.95 vs 0.75) but also in identifying SCD patients with biological hallmarks of AD in the cerebrospinal fluid (recall 0.87 vs 0.50).

DISCUSSION

Personalized models could (1) support classification of MCI, (2) assess the presence of AD pathology, and (3) estimate the risk of cognitive decline progression, based only on economical and non-invasive EEG recordings.

Highlights

Personalized cortical model estimating structural alterations from EEG recordings.Discrimination of Mild Cognitive Impairment (MCI) and Healthy (HC) subjects (95%)Prediction of biological markers of Alzheimer's in Subjective Decline (SCD) Subjects (87%)Transition correctly predicted for 3/3 subjects that converted from SCD to MCI after 1y.

Altered synaptic plasticity at hippocampal CA1-CA3 synapses in Alzheimer's disease: integration of amyloid precursor protein intracellular domain and amyloid beta effects into computational models

Alzheimer's disease (AD) is a progressive memory loss and cognitive dysfunction brain disorder brought on by the dysfunctional amyloid precursor protein (APP) processing and clearance of APP peptides. Increased APP levels lead to the production of AD-related peptides including the amyloid APP intracellular domain (AICD) and amyloid beta (Aβ), and consequently modify the intrinsic excitability of the hippocampal CA1 pyramidal neurons, synaptic protein activity, and impair synaptic plasticity at hippocampal CA1-CA3 synapses. The goal of the present study is to build computational models that incorporate the effect of AD-related peptides on CA1 pyramidal neuron and hippocampal synaptic plasticity under the AD conditions and investigate the potential pharmacological treatments that could normalize hippocampal synaptic plasticity and learning in AD. We employ a phenomenological N-methyl-D-aspartate (NMDA) receptor-based voltage-dependent synaptic plasticity model that includes the separate receptor contributions on long-term potentiation (LTP) and long-term depression (LTD) and embed it into the a detailed compartmental model of CA1 pyramidal neuron. Modeling results show that partial blockade of Glu2NB-NMDAR-gated channel restores intrinsic excitability of a CA1 pyramidal neuron and rescues LTP in AICD and Aβ conditions. The model provides insight into the complex interactions in AD pathophysiology and suggests the conditions under which the synchronous activation of a cluster of synaptic inputs targeting the dendritic tree of CA1 pyramidal neuron leads to restored synaptic plasticity.

Whole-brain modeling of the differential influences of amyloid-beta and tau in Alzheimer's disease

BACKGROUND

Alzheimer's disease is a neurodegenerative condition associated with the accumulation of two misfolded proteins, amyloid-beta (A[Formula: see text]) and tau. We study their effect on neuronal activity, with the aim of assessing their individual and combined impact.

METHODS

We use a whole-brain dynamic model to find the optimal parameters that best describe the effects of A[Formula: see text] and tau on the excitation-inhibition balance of the local nodes.

RESULTS

We found a clear dominance of A[Formula: see text] over tau in the early disease stages (MCI), while tau dominates over A[Formula: see text] in the latest stages (AD). We identify crucial roles for A[Formula: see text] and tau in complex neuronal dynamics and demonstrate the viability of using regional distributions to define models of large-scale brain function in AD.

CONCLUSIONS

Our study provides further insight into the dynamics and complex interplay between these two proteins, opening the path for further investigations on biomarkers and candidate therapeutic targets in-silico.

In Vivo Near-Infrared Fluorescence Imaging Selective for Soluble Amyloid β Aggregates Using y-Shaped BODIPY Derivative

Soluble amyloid β (Aβ) aggregates, suggested to be the most toxic forms of Aβ, draw attention as therapeutic targets and biomarkers of Alzheimer's disease (AD). As soluble Aβ aggregates are transient and diverse, imaging their diverse forms in vivo is expected to have a marked impact on research and diagnosis of AD. Herein, we report a near-infrared fluorescent (NIRF) probe, BAOP-16, targeting diverse soluble Aβ aggregates. BAOP-16, whose molecular shape resembles "y", showed a marked selective increase in fluorescence intensity upon binding to soluble Aβ aggregates in the near-infrared region and a high binding affinity for them. Additionally, BAOP-16 could detect Aβ oligomers in the brains of Aβ-inoculated model mice. In an in vivo fluorescence imaging study of BAOP-16, brains of AD model mice displayed significantly higher fluorescence signals than those of wild-type mice. These results indicate that BAOP-16 could be useful for the in vivo NIRF imaging of diverse soluble Aβ aggregates.

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.

Combining aperiodic 1/f slopes and brain simulation: An EEG/MEG proxy marker of excitation/inhibition imbalance in Alzheimer's disease

INTRODUCTION

Accumulation and interaction of amyloid-beta (Aβ) and tau proteins during progression of Alzheimer's disease (AD) are shown to tilt neuronal circuits away from balanced excitation/inhibition (E/I). Current available techniques for noninvasive interrogation of E/I in the intact human brain, for example, magnetic resonance spectroscopy (MRS), are highly restrictive (i.e., limited spatial extent), have low temporal and spatial resolution and suffer from the limited ability to distinguish accurately between different neurotransmitters complicating its interpretation. As such, these methods alone offer an incomplete explanation of E/I. Recently, the aperiodic component of neural power spectrum, often referred to in the literature as the '1/f slope', has been described as a promising and scalable biomarker that can track disruptions in E/I potentially underlying a spectrum of clinical conditions, such as autism, schizophrenia, or epilepsy, as well as developmental E/I changes as seen in aging.

METHODS

Using 1/f slopes from resting-state spectral data and computational modeling, we developed a new method for inferring E/I alterations in AD.

RESULTS

We tested our method on recent freely and publicly available electroencephalography (EEG) and magnetoencephalography (MEG) datasets of patients with AD or prodromal disease and demonstrated the method's potential for uncovering regional patterns of abnormal excitatory and inhibitory parameters.

DISCUSSION

Our results provide a general framework for investigating circuit-level disorders in AD and developing therapeutic interventions that aim to restore the balance between excitation and inhibition.

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.

Natural product-based pharmacological studies for neurological disorders

Central nervous system (CNS) disorders and diseases are expected to rise sharply in the coming years, partly because of the world's aging population. Medicines for the treatment of the CNS have not been successfully made. Inadequate knowledge about the brain, pharmacokinetic and dynamic errors in preclinical studies, challenges with clinical trial design, complexity and variety of human brain illnesses, and variations in species are some potential scenarios. Neurodegenerative diseases (NDDs) are multifaceted and lack identifiable etiological components, and the drugs developed to treat them did not meet the requirements of those who anticipated treatments. Therefore, there is a great demand for safe and effective natural therapeutic adjuvants. For the treatment of NDDs and other memory-related problems, many herbal and natural items have been used in the Ayurvedic medical system. Anxiety, depression, Parkinson's, and Alzheimer's diseases (AD), as well as a plethora of other neuropsychiatric disorders, may benefit from the use of plant and food-derived chemicals that have antidepressant or antiepileptic properties. We have summarized the present level of knowledge about natural products based on topological evidence, bioinformatics analysis, and translational research in this review. We have also highlighted some clinical research or investigation that will help us select natural products for the treatment of neurological conditions. In the present review, we have explored the potential efficacy of phytoconstituents against neurological diseases. Various evidence-based studies and extensive recent investigations have been included, which will help pharmacologists reduce the progression of neuronal disease.

Ethical considerations for precision psychiatry: A roadmap for research and clinical practice

Precision psychiatry is an emerging field with transformative opportunities for mental health. However, the use of clinical prediction models carries unprecedented ethical challenges, which must be addressed before accessing the potential benefits of precision psychiatry. This critical review covers multidisciplinary areas, including psychiatry, ethics, statistics and machine-learning, healthcare and academia, as well as input from people with lived experience of mental disorders, their family, and carers. We aimed to identify core ethical considerations for precision psychiatry and mitigate concerns by designing a roadmap for research and clinical practice. We identified priorities: learning from somatic medicine; identifying precision psychiatry use cases; enhancing transparency and generalizability; fostering implementation; promoting mental health literacy; communicating risk estimates; data protection and privacy; and fostering the equitable distribution of mental health care. We hope this blueprint will advance research and practice and enable people with mental health problems to benefit from precision psychiatry.

The quest for multiscale brain modeling

Addressing the multiscale organization of the brain, which is fundamental to the dynamic repertoire of the organ, remains challenging. In principle, it should be possible to model neurons and synapses in detail and then connect them into large neuronal assemblies to explain the relationship between microscopic phenomena, large-scale brain functions, and behavior. It is more difficult to infer neuronal functions from ensemble measurements such as those currently obtained with brain activity recordings. In this article we consider theories and strategies for combining bottom-up models, generated from principles of neuronal biophysics, with top-down models based on ensemble representations of network activity and on functional principles. These integrative approaches are hoped to provide effective multiscale simulations in virtual brains and neurorobots, and pave the way to future applications in medicine and information technologies.

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

Increasing evidence suggests that measures of signal variability and complexity could present promising biomarkers for Alzheimer's disease (AD). Earlier studies have however been limited to the characterization of local activity. Here, we investigate whether a network version of permutation entropy could serve as a novel biomarker for early-stage AD. Resting-state source-space magnetoencephalography was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Local activity was characterized by permutation entropy (PE). Network-level interactions were studied using the inverted joint permutation entropy (JPEinv), corrected for volume conduction. The JPEinv showed a reduction of nonlinear connectivity in MCI subjects in the theta and alpha band. Local PE showed increased theta band entropy. Between-group differences were widespread across brain regions. Receiver operating characteristic (ROC) analysis of classification of MCI versus SCD subjects revealed that a logistic regression model trained on JPEinv features (78.4% [62.5-93.3%]) slightly outperformed PE (76.9% [60.3-93.4%]) and relative theta power-based models (76.9% [60.4-93.3%]). Classification performance of theta JPEinv was at least as good as the relative theta power benchmark. The JPEinv is therefore a potential biomarker for early-stage AD that should be explored in larger studies.

Linking Brain Structure, Activity, and Cognitive Function through Computation

Understanding the human brain is a "Grand Challenge" for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generative multiscale models, which enable the investigation of causation across scales and are guided by principles and theories of brain function, are instrumental for linking brain structure and function. An example of a resource enabling such an integrated approach to neuroscientific discovery is the BigBrain, which spatially anchors tissue models and data across different scales and ensures that multiscale models are supported by the data, making the bridge to both basic neuroscience and medicine. Research at the intersection of neuroscience, computing and robotics has the potential to advance neuro-inspired technologies by taking advantage of a growing body of insights into perception, plasticity and learning. To render data, tools and methods, theories, basic principles and concepts interoperable, the Human Brain Project (HBP) has launched EBRAINS, a digital neuroscience research infrastructure, which brings together a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating insights and perspectives for societal benefits.