Brain network modulation in Alzheimer's and frontotemporal dementia with transcranial electrical stimulation

A Critical Review of Noninvasive Brain Stimulation Technologies in Alzheimer's Dementia and Primary Progressive Aphasia

Multiple pharmacologic agents now have been approved in the United States and other countries as treatment to slow disease and clinical progression for Alzheimer's disease. Given these treatments have not been proven to lessen the cognitive deficits already manifested in the Alzheimer's Clinical Syndrome (ACS), and none are aimed for another debilitating dementia syndrome identified as primary progressive aphasia (PPA), there is an urgent need for new, safe, tolerable, and efficacious treatments to mitigate the cognitive deficits experienced in ACS and PPA. Noninvasive brain stimulation has shown promise for enhancing cognitive functioning, and there has been interest in its potential therapeutic value in ACS and PPA. This review critically examines the evidence of five technologies in ACS and PPA: transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), transcranial random noise stimulation (tRNS), repetitive transcranial magnetic stimulation (rTMS), and noninvasive vagus nerve stimulation (nVNS). Many randomized controlled trials of tDCS and rTMS report positive treatment effects on cognition in ACS and PPA that persist out to at least 8 weeks, whereas there are few trials for tACS and none for tRNS and nVNS. However, most positive trials did not identify clinically meaningful changes, underscoring that clinical efficacy has yet to be established in ACS and PPA. Much is still to be learned about noninvasive brain stimulation in ACS and PPA, and shifting the focus to prioritize clinical significance in addition to statistical significance in trials could yield greater success in understanding its potential cognitive effects and optimal parameters.

Long-term, home-based transcranial direct current stimulation coupled with computerized cognitive training in frontotemporal dementia: A case report

We present the case of a 62-year-old woman with probable behavioral variant of frontotemporal dementia (bvFTD) with cognitive/language deficits who demonstrated improved performance on cognitive/language testing and in functional tasks following long-term, home-based transcranial direct current stimulation (tDCS) coupled with computerized cognitive training (CCT). The patient underwent home-based tDCS (anode on the left prefrontal cortex and cathode on the right homologue) for 46 sessions over 10 weeks along with CCT. On post-treatment testing, the patient improved by 3 points on the Mini-Mental State Exam (MMSE) (23 to 26). She also showed improvement on several cognitive/language tasks, such as immediate recall of single words and word pairs, total accurate words in sentence repetition, delayed recall, semantic processing, and sentence level comprehension. There was no decline in several other cognitive and language tasks. Family members reported subjective improvements in expressiveness, communication, and interaction with others as well as increased attention to grooming and style which contrasted with her pre-treatment condition. This report suggests that home-based tDCS combined with CCT for an extended period may slow decline, and improve cognitive/language performance and everyday function in FTD.

[Paradoxical lucidity and personal identity in patients with advanced dementia: change of paradigm?]

In this article we approach the concept of paradoxical lucidity (LP) (an unexpected, spontaneous, significant and relevant episode of communication or connection) in persons with advanced dementia. The existence of LP could change the paradigm of dementia as a degenerative, chronic, progressive and irreversible disease (where neuronal death plays the leading role), towards a model where functional deficits of neuronal networks acquire importance, which raises new potentially reversible therapeutic and rehabilitative possibilities. We analyze the ethical consequences that these episodes may have with respect to the implicated persons (patients, caregivers and professionals in charge of their care) and try to answer the following question: Do persons with advanced dementia continue to maintain their personal identity despite suffering cognitive impairment so severe?. The LP indicates that this is possible. In this work we make a transversal outline of the different concepts and theories of personal identity in these patients, from different areas of knowledge (philosophy, psychology, neuroscience).

Transcriptomic analysis of rat brain response to alternating current electrical stimulation: unveiling insights via single-nucleus RNA sequencing

Electrical brain stimulation (EBS) has gained popularity for laboratory and clinical applications. However, comprehensive characterization of cellular diversity and gene expression changes induced by EBS remains limited, particularly with respect to specific brain regions and stimulation sites. Here, we presented the initial single-nucleus RNA sequencing profiles of rat cortex, hippocampus, and thalamus subjected to intracranial alternating current stimulation (iACS) at 40 Hz. The results demonstrated an increased number of neurons in all three regions in response to iACS. Interestingly, less than 0.1% of host gene expression in neurons was significantly altered by iACS. In addition, we identified Rgs9, a known negative regulator of dopaminergic signaling, as a unique downregulated gene in neurons. Unilateral iACS produced a more focused local effect in attenuating the proportion of Rgs9+ neurons in the ipsilateral compared to bilateral iACS treatment. The results suggested that unilateral iACS at 40 Hz was an efficient approach to increase the number of neurons and downregulate Rgs9 gene expression without affecting other cell types or genes in the brain. Our study presented the direct evidence that EBS could boost cerebral neurogenesis and enhance neuronal sensitization to dopaminergic drugs and agonists, through its downregulatory effect on Rgs9 in neurons.

A Study on the Effect of Executive Control Network Functional Connection on the Therapeutic Efficacy of Repetitive Transcranial Magnetic Stimulation in Alzheimer's Disease

Background

Alzheimer's disease (AD) is a neurodegenerative disease characterized by brain network dysfunction. Few studies have investigated whether the functional connections between executive control networks (ECN) and other brain regions can predict the therapeutic effect of repetitive transcranial magnetic stimulation (rTMS).

Objective

The purpose of this study is to examine the relationship between the functional connectivity (FC) within ECN networks and the efficacy of rTMS.

Methods

We recruited AD patients for rTMS treatment. We established an ECN using baseline period fMRI data and conducted an analysis of the ECN's FC throughout the brain. Concurrently, the support vector regression (SVR) method was employed to project post-rTMS cognitive scores, utilizing the connectional attributes of the ECN as predictive markers.

Results

The average age of the patients was 66.86±8.44 years, with 8 males and 13 females. Significant improvement on most cognitive measures. We use ECN connectivity and brain region functions in baseline patients as features for SVR model training and fitting. The SVR model could demonstrate significant predictability for changes in Montreal Cognitive Assessment scores among AD patients after rTMS treatment. The brain regions that contributed most to the prediction of the model (the top 10% of weights) were located in the medial temporal lobe, middle temporal gyrus, frontal lobe, parietal lobe and occipital lobe.

Conclusions

The stronger the antagonism between ECN and parieto-occipital lobe function, the better the prediction of cognitive improvement; the stronger the synergy between ECN and fronto-temporal lobe function, the better the prediction of cognitive improvement.

Prediction of misfolded proteins spreading in Alzheimer's disease using machine learning and spreading models

The pervasive impact of Alzheimer's disease on aging society represents one of the main challenges at this time. Current investigations highlight 2 specific misfolded proteins in its development: Amyloid-$\beta$ and tau. Previous studies focused on spreading for misfolded proteins exploited simulations, which required several parameters to be empirically estimated. Here, we provide an alternative view based on 2 machine learning approaches which we compare with known simulation models. The first approach applies an autoregressive model constrained by structural connectivity, while the second is based on graph convolutional networks. The aim is to predict concentrations of Amyloid-$\beta$ 2 yr after a provided baseline. We also evaluate its real-world effectiveness and suitability by providing a web service for physicians and researchers. In experiments, the autoregressive model generally outperformed state-of-the-art models resulting in lower prediction errors. While it is important to note that a comprehensive prognostic plan cannot solely rely on amyloid beta concentrations, their prediction, achieved by the discussed approaches, can be valuable for planning therapies and other cures, especially when dealing with asymptomatic patients for whom novel therapies could prove effective.

The effects of noninvasive brain stimulation on cognitive function in patients with mild cognitive impairment and Alzheimer's disease using resting-state functional magnetic resonance imaging: A systematic review and meta-analysis

OBJECTIVE

The aim of this systematic review and meta-analysis was to evaluate the efficacy of noninvasive brain stimulation (NIBS) on cognition using functional magnetic resonance imaging (fMRI) in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD), thus providing the neuroimaging mechanism of cognitive intervention.

METHODS

English articles published up to April 30, 2023 were searched in the PubMed, Web of Science, Embase, and Cochrane Library databases. We included randomized controlled trials where resting-state fMRI was used to observe the effect of NIBS in patients with MCI or AD. RevMan software was used to analyze the continuous variables, and SDM-PSI software was used to perform an fMRI data analysis.

RESULTS

A total of 17 studies comprising 258 patients in the treatment group and 256 in the control group were included. After NIBS, MCI patients in the treatment group showed hyperactivation in the right precuneus and decreased activity in the left cuneus and right supplementary motor area. In contrast, patients in the control group showed decreased activity in the right middle frontal gyrus and no hyperactivation. The clinical cognitive scores in MCI patients were significantly improved by NIBS, while not in AD. Some evidence regarding the modulation of NIBS in resting-state brain activity and functional brain networks in patients with AD was found.

CONCLUSIONS

NIBS could improve cognitive function in patients with MCI and AD. fMRI evaluations could be added to evaluate the contribution of specific NIBS treatment therapeutic effectiveness.

Identifying biomarkers for tDCS treatment response in Alzheimer's disease patients: a machine learning approach using resting-state EEG classification

Background

Transcranial direct current stimulation (tDCS) is a promising treatment for Alzheimer's Disease (AD). However, identifying objective biomarkers that can predict brain stimulation efficacy, remains a challenge. The primary aim of this investigation is to delineate the cerebral regions implicated in AD, taking into account the existing lacuna in comprehension of these regions. In pursuit of this objective, we have employed a supervised machine learning algorithm to prognosticate the neurophysiological outcomes resultant from the confluence of tDCS therapy plus cognitive intervention within both the cohort of responders and non-responders to antecedent tDCS treatment, stratified on the basis of antecedent cognitive outcomes.

Methods

The data were obtained through an interventional trial. The study recorded high-resolution electroencephalography (EEG) in 70 AD patients and analyzed spectral power density during a 6 min resting period with eyes open focusing on a fixed point. The cognitive response was assessed using the AD Assessment Scale-Cognitive Subscale. The training process was carried out through a Random Forest classifier, and the dataset was partitioned into K equally-partitioned subsamples. The model was iterated k times using K-1 subsamples as the training bench and the remaining subsample as validation data for testing the model.

Results

A clinical discriminating EEG biomarkers (features) was found. The ML model identified four brain regions that best predict the response to tDCS associated with cognitive intervention in AD patients. These regions included the channels: FC1, F8, CP5, Oz, and F7.

Conclusion

These findings suggest that resting-state EEG features can provide valuable information on the likelihood of cognitive response to tDCS plus cognitive intervention in AD patients. The identified brain regions may serve as potential biomarkers for predicting treatment response and maybe guide a patient-centered strategy.

Clinical Trial Registration

https://classic.clinicaltrials.gov/ct2/show/NCT02772185?term=NCT02772185&draw=2&rank=1, identifier ID: NCT02772185.

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.

Social and non-social working memory in neurodegeneration

Although social functioning relies on working memory, whether a social-specific mechanism exists remains unclear. This undermines the characterization of neurodegenerative conditions with both working memory and social deficits. We assessed working memory domain-specificity across behavioral, electrophysiological, and neuroimaging dimensions in 245 participants. A novel working memory task involving social and non-social stimuli with three load levels was assessed across controls and different neurodegenerative conditions with recognized impairments in: working memory and social cognition (behavioral-variant frontotemporal dementia); general cognition (Alzheimer's disease); and unspecific patterns (Parkinson's disease). We also examined resting-state theta oscillations and functional connectivity correlates of working memory domain-specificity. Results in controls and all groups together evidenced increased working memory demands for social stimuli associated with frontocinguloparietal theta oscillations and salience network connectivity. Canonical frontal theta oscillations and executive-default mode network anticorrelation indexed non-social stimuli. Behavioral-variant frontotemporal dementia presented generalized working memory deficits related to posterior theta oscillations, with social stimuli linked to salience network connectivity. In Alzheimer's disease, generalized working memory impairments were related to temporoparietal theta oscillations, with non-social stimuli linked to the executive network. Parkinson's disease showed spared working memory performance and canonical brain correlates. Findings support a social-specific working memory and related disease-selective pathophysiological mechanisms.

Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders

Neuroimaging has revolutionized our understanding of brain function and has become an essential tool for researchers studying neurological disorders. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are two widely used neuroimaging techniques to review changes in brain activity. fMRI is a noninvasive technique that uses magnetic fields and radio waves to produce detailed brain images. An EEG is a noninvasive technique that records the brain's electrical activity through electrodes placed on the scalp. This review overviews recent developments in noninvasive functional neuroimaging methods, including fMRI and EEG. Recent advances in fMRI technology, its application to studying brain function, and the impact of neuroimaging techniques on neuroscience research are discussed. Advances in EEG technology and its applications to analyzing brain function and neural oscillations are also highlighted. In addition, advanced courses in neuroimaging, such as diffusion tensor imaging (DTI) and transcranial electrical stimulation (TES), are described, along with their role in studying brain connectivity, white matter tracts, and potential treatments for schizophrenia and chronic pain. Application. The review concludes by examining neuroimaging studies of neurodevelopmental and neurological disorders such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease (AD), and Parkinson's disease (PD). We also described the role of transcranial direct current stimulation (tDCS) in ASD, ADHD, AD, and PD. Neuroimaging techniques have significantly advanced our understanding of brain function and provided essential insights into neurological disorders. However, further research into noninvasive treatments such as EEG, MRI, and TES is necessary to continue to develop new diagnostic and therapeutic strategies for neurological disorders.

Model-based whole-brain perturbational landscape of neurodegenerative diseases

The treatment of neurodegenerative diseases is hindered by lack of interventions capable of steering multimodal whole-brain dynamics towards patterns indicative of preserved brain health. To address this problem, we combined deep learning with a model capable of reproducing whole-brain functional connectivity in patients diagnosed with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). These models included disease-specific atrophy maps as priors to modulate local parameters, revealing increased stability of hippocampal and insular dynamics as signatures of brain atrophy in AD and bvFTD, respectively. Using variational autoencoders, we visualized different pathologies and their severity as the evolution of trajectories in a low-dimensional latent space. Finally, we perturbed the model to reveal key AD- and bvFTD-specific regions to induce transitions from pathological to healthy brain states. Overall, we obtained novel insights on disease progression and control by means of external stimulation, while identifying dynamical mechanisms that underlie functional alterations in neurodegeneration.

Altered Grey Matter-Brain Healthcare Quotient: Interventions of Olfactory Training and Learning of Neuroplasticity

Recent studies revealed that grey matter (GM) changes due to various training and learning experiences, using magnetic resonance imaging. In this study, we investigate the effect of psychological characteristics and attitudes toward training and learning on GM changes. Ninety participants were recruited and distributed into three groups: an olfactory training group that underwent 40 olfactory training sessions designed for odour classification tasks, a group classified for learning of neuroplasticity and brain healthcare using a TED Talk video and 28 daily brain healthcare messages, and a control group. Further, we assessed psychological characteristics, such as curiosity and personal growth initiatives. In the olfactory training group, we conducted a questionnaire survey on olfactory training regarding their interests and sense of accomplishment. In the olfactory training group, the GM change was significantly correlated with the sense of achievement and interest in training. The learning of neuroplasticity and brain healthcare group showed a significantly smaller 2-month GM decline than did the control group. The Curiosity and Exploration Inventory-II scores were significantly correlated with GM changes in both intervention groups only. In conclusion, our result suggested that training or learning with a sense of accomplishment, interest, and curiosity would lead to greater GM changes.

A low-dimensional cognitive-network space in Alzheimer's disease and frontotemporal dementia

BACKGROUND

Alzheimer's disease (AD) and frontotemporal dementia (FTD) show network dysfunctions linked with cognitive deficits. Within this framework, network abnormalities between AD and FTD show both convergent and divergent patterns. However, these functional patterns are far from being established and their relevance to cognitive processes remains to be elucidated.

METHODS

We investigated the relationship between cognition and functional connectivity of major cognitive networks in these diseases. Twenty-three bvFTD (age: 71±10), 22 AD (age: 72±6), and 20 controls (age: 72±6) underwent cognitive evaluation and resting-state functional MRI. Principal component analysis was used to describe cognitive variance across participants. Brain network connectivity was estimated with connectome analysis. Connectivity matrices were created assessing correlations between parcels within each functional network. The following cognitive networks were considered: default mode (DMN), dorsal attention (DAN), ventral attention (VAN), and frontoparietal (FPN) networks. The relationship between cognition and connectivity was assessed using a bootstrapping correlation and interaction analyses.

RESULTS

Three principal cognitive components explained more than 80% of the cognitive variance: the first component (cogPC1) loaded on memory, the second component (cogPC2) loaded on emotion and language, and the third component (cogPC3) loaded on the visuo-spatial and attentional domains. Compared to HC, AD and bvFTD showed impairment in all cogPCs (p<0.002), and bvFTD scored worse than AD in cogPC2 (p=0.031). At the network level, the DMN showed a significant association in the whole group with cogPC1 and cogPC2 and the VAN with cogPC2. By contrast, DAN and FPN showed a divergent pattern between diagnosis and connectivity for cogPC2. We confirmed these results by means of a multivariate analysis (canonical correlation).

CONCLUSIONS

A low-dimensional representation can account for a large variance in cognitive scores in the continuum from normal to pathological aging. Moreover, cognitive components showed both convergent and divergent patterns with connectivity across AD and bvFTD. The convergent pattern was observed across the networks primarily involved in these diseases (i.e., the DMN and VAN), while a divergent FC-cognitive pattern was mainly observed between attention/executive networks and the language/emotion cognitive component, suggesting the co-existence of compensatory and detrimental mechanisms underlying these components.