Neurophysiological markers of network dysfunction in neurodegenerative diseases

Exploring easily accessible neurophysiological biomarkers for predicting Alzheimer's disease progression: a systematic review

Alzheimer disease (AD) remains a significant global health concern. The progression from preclinical stages to overt dementia has become a crucial point of interest for researchers. This paper reviews the potential of neurophysiological biomarkers in predicting AD progression, based on a systematic literature search following PRISMA guidelines, including 55 studies. EEG-based techniques have been predominantly employed, whereas TMS studies are less common. Among the investigated neurophysiological measures, spectral power measurements and event-related potentials-based measures, including P300 and N200 latencies, have emerged as the most consistent and reliable biomarkers for predicting the likelihood of conversion to AD. In addition, TMS-based indices of cortical excitability and synaptic plasticity have also shown potential in assessing the risk of conversion to AD. However, concerns persist regarding the methodological discrepancies among studies, the accuracy of these neurophysiological measures in comparison to established AD biomarkers, and their immediate clinical applicability. Further research is needed to validate the predictive capabilities of EEG and TMS measures. Advancements in this area could lead to cost-effective, reliable biomarkers, enhancing diagnostic processes and deepening our understanding of AD pathophysiology.

Decoding aging and cognitive functioning through spatiotemporal EEG patterns: Introducing spatiotemporal information-based similarity analysis

Exploring spatiotemporal patterns of high-dimensional electroencephalography (EEG) time series generated from complex brain system is crucial for deciphering aging and cognitive functioning. Analyzing high-dimensional EEG series poses challenges, particularly when employing distance-based methods for spatiotemporal dynamics. Therefore, we proposed an innovative methodology for multi-channel EEG data, termed as Spatiotemporal Information-based Similarity (STIBS) analysis. The core of this method is to first perform state space compression of multi-channel EEG time series using global field power, which can provide insight into the dynamic integration of spatiotemporal patterns between the steady states and non-steady states of brain. Subsequently, we quantify the pairwise differences and non-randomness of spatiotemporal patterns using an information-based similarity analysis. Results demonstrated that this method holds the potential to serve as a distinguishing marker between young and elderly on both pairwise differences and non-randomness indices. Young individuals and those with higher cognitive abilities exhibit more complex macrostructure and non-random spatiotemporal patterns, whereas both aging and cognitive decline lead to more randomized spatiotemporal patterns. We further extended the proposed analytics to brain regions adversarial STIBS (bra-STIBS), highlighting differences between young and elderly, as well as high and low cognitive groups. Furthermore, utilizing the STIBS-based XGBoost model yields superior recognition accuracy in aging (93.05%) and cognitive functioning (74.29%, 64.19%, and 80.28%, respectively, for attention, memory, and compatibility performance recognition). STIBS-based methodology not only contributes to the ongoing exploration of neurobiological changes in aging but also provides a powerful tool for characterizing the spatiotemporal nonlinear dynamics of the brain and their implications for cognitive functioning.

Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms

From Alzheimer's disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient's lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain's molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.

Electroencephalographic β-band oscillations in the sensorimotor network reflect motor symptom severity in amyotrophic lateral sclerosis

BACKGROUND AND PURPOSE

Resting-state electroencephalography (EEG) holds promise for assessing brain networks in amyotrophic lateral sclerosis (ALS). We investigated whether neural β-band oscillations in the sensorimotor network could serve as an objective quantitative measure of progressive motor impairment and functional disability in ALS patients.

METHODS

Resting-state EEG was recorded in 18 people with ALS and 38 age- and gender-matched healthy controls. We estimated source-localized β-band spectral power in the sensorimotor cortex. Clinical evaluation included lower (LMN) and upper motor neuron scores, Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised score, fine motor function (FMF) subscore, and progression rate. Correlations between clinical scores and β-band power were analysed and corrected using a false discovery rate of q = 0.05.

RESULTS

β-Band power was significantly lower in people with ALS than controls (p = 0.004), and correlated with LMN score (R = -0.65, p = 0.013), FMF subscore (R = -0.53, p = 0.036), and FMF progression rate (R = 0.52, p = 0.036).

CONCLUSIONS

β-Band spectral power in the sensorimotor cortex reflects clinically evaluated motor impairment in ALS. This technology merits further investigation as a biomarker of progressive functional disability.

Examining short interval intracortical inhibition with different transcranial magnetic stimulation-induced current directions in ALS

Objective

To establish if induced current direction across the motor cortex alters the sensitivity of transcranial magnetic stimulation (TMS)-evoked short-interval intracortical inhibition (SICI) as an ALS biomarker.

Methods

Threshold tracking-TMS was undertaken in 35 people with ALS and 39 controls. Using a coil orientation which induces posterior-anterior (PA)-directed current across the motor cortex, SICI (1 ms and 3 ms interstimulus intervals) and intracortical facilitation (ICF, 10 ms interstimulus interval) were recorded. SICI3ms was also recorded using a coil orientation which induces anterior-posterior (AP)-directed current across the motor cortex.

Results

At group level, SICI3ms-PA (AUROC = 0.7), SICI3ms-AP (AUROC = 0.8) and SICI1ms (AUROC = 0.66) were substantially lower in those with ALS, although there was considerable interindividual heterogeneity. Averaging across interstimulus intervals (ISIs) marginally improved SICIPA sensitivity (AUROC = 0.76). Averaging SICI values across ISIs and orientations into a single SICI measure did not substantially improve sensitivity (AUROC = 0.81) compared to SICI3ms-AP alone. SICI3ms-AP and SICI3ms-PA did not significantly correlate (rho = 0.19, p = 0.313), while SICI1ms-PA and SICI3ms-PA did (rho = 0.37, p = 0.006). Further, those with ALS with the lowest SICI3ms-PA were not those with the lowest SICI3ms-AP. ICF was similar between groups (AUROC = 0.50).

Conclusions

SICIPA and SICIAP are uncorrelated measures of motor cortical inhibitory functions which are useful as distinct, unequally affected, measures of disinhibition in ALS.

Significance

Examining both SICIPA and SICIAP may facilitate more comprehensive characterisation of motor cortical disinhibition in ALS.

Contingent Negative Variation in the Evaluation of Neurocognitive Disorders Due to Possible Alzheimer's Disease

The usefulness of Contingent Negative Variation (CNV) potential as a biomarker of neurocognitive disorders due to possible Alzheimer's disease, is based on its possible physiological correlates. However, its application in the diagnostic evaluation of these disorders is still incipient. The aim of this study is to characterize the patterns of cognitive processing of information in the domain of nonspecific global attention, by recording potential CNV in a group of patients with neurocognitive disorders due to possible Alzheimer's disease. An experimental study of cases and controls was carried out. The sample included 39 patients classified according to DSM-5 with a neurocognitive disorder subtype possibly due Alzheimer's disease, and a Control Group of 53 subjects with normal cognitive functions. CNV potential was registered using standard protocol. The analysis of variance obtained significant differences in mean values and confidence intervals of total CNV amplitude between the three study groups. The late CNV segment amplitudes makes it possible to discriminate between the level of mild and major dysfunction in the group of patients. The CNV total amplitudes of potential allows for effective discrimination between normal cognitive functioning and neurocognitive disorders due to possible Alzheimer's disease.

Biomarkers in amyotrophic lateral sclerosis: current status and future prospects

Disease heterogeneity in amyotrophic lateral sclerosis poses a substantial challenge in drug development. Categorization based on clinical features alone can help us predict the disease course and survival, but quantitative measures are also needed that can enhance the sensitivity of the clinical categorization. In this Review, we describe the emerging landscape of diagnostic, categorical and pharmacodynamic biomarkers in amyotrophic lateral sclerosis and their place in the rapidly evolving landscape of new therapeutics. Fluid-based markers from cerebrospinal fluid, blood and urine are emerging as useful diagnostic, pharmacodynamic and predictive biomarkers. Combinations of imaging measures have the potential to provide important diagnostic and prognostic information, and neurophysiological methods, including various electromyography-based measures and quantitative EEG-magnetoencephalography-evoked responses and corticomuscular coherence, are generating useful diagnostic, categorical and prognostic markers. Although none of these biomarker technologies has been fully incorporated into clinical practice or clinical trials as a primary outcome measure, strong evidence is accumulating to support their clinical utility.

Multiple neuron clusters on Micro-Electrode Arrays as an in vitro model of brain network

Understanding the brain functioning is essential for governing brain processes with the aim of managing pathological network dysfunctions. Due to the morphological and biochemical complexity of the central nervous system, the development of general models with predictive power must start from in vitro brain network engineering. In the present work, we realized a micro-electrode array (MEA)-based in vitro brain network and studied its emerging dynamical properties. We obtained four-neuron-clusters (4N) assemblies by plating rat embryo cortical neurons on 60-electrode MEA with cross-shaped polymeric masks and compared the emerging dynamics with those of sister single networks (1N). Both 1N and 4N assemblies exhibited spontaneous electrical activity characterized by spiking and bursting signals up to global activation by means of network bursts. Data revealed distinct patterns of network activity with differences between 1 and 4N. Rhythmic network bursts and dominant initiator clusters suggested pacemaker activities in both assembly types, but the propagation of activation sequences was statistically influenced by the assembly topology. We proved that this rhythmic activity was ivabradine sensitive, suggesting the involvement of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, and propagated across the real clusters of 4N, or corresponding virtual clusters of 1N, with dominant initiator clusters, and nonrandom cluster activation sequences. The occurrence of nonrandom series of identical activation sequences in 4N revealed processes possibly ascribable to neuroplasticity. Hence, our multi-network dissociated cortical assemblies suggest the relevance of pacemaker neurons as essential elements for generating brain network electrophysiological patterns; indeed, such evidence should be considered in the development of computational models for envisaging network behavior both in physiological and pathological conditions.

Paving the Way for Predicting the Progression of Cognitive Decline: The Potential Role of Machine Learning Algorithms in the Clinical Management of Neurodegenerative Disorders

Alzheimer's disease (AD) is the most common form of neurodegenerative disorder. The prodromal phase of AD is mild cognitive impairment (MCI). The capacity to predict the transitional phase from MCI to AD represents a challenge for the scientific community. The adoption of artificial intelligence (AI) is useful for diagnostic, predictive analysis starting from the clinical epidemiology of neurodegenerative disorders. We propose a Machine Learning Model (MLM) where the algorithms were trained on a set of neuropsychological, neurophysiological, and clinical data to predict the diagnosis of cognitive decline in both MCI and AD patients.

METHODS

We built a dataset with clinical and neuropsychological data of 4848 patients, of which 2156 had a diagnosis of AD, and 2684 of MCI, for the Machine Learning Model, and 60 patients were enrolled for the test dataset. We trained an ML algorithm using RoboMate software based on the training dataset, and then calculated its accuracy using the test dataset.

RESULTS

The Receiver Operating Characteristic (ROC) analysis revealed that diagnostic accuracy was 86%, with an appropriate cutoff value of 1.5; sensitivity was 72%; and specificity reached a value of 91% for clinical data prediction with MMSE.

CONCLUSION

This method may support clinicians to provide a second opinion concerning high prognostic power in the progression of cognitive impairment. The MLM used in this study is based on big data that were confirmed in enrolled patients and given a credibility about the presence of determinant risk factors also supported by a cognitive test score.

Transcranial focused ultrasound stimulation of cortical and thalamic somatosensory areas in human

The effects of transcranial focused ultrasound (FUS) stimulation of the primary somatosensory cortex and its thalamic projection (i.e., ventral posterolateral nucleus) on the generation of electroencephalographic (EEG) responses were evaluated in healthy human volunteers. Stimulation of the unilateral somatosensory circuits corresponding to the non-dominant hand generated EEG evoked potentials across all participants; however, not all perceived stimulation-mediated tactile sensations of the hand. These FUS-evoked EEG potentials (FEP) were observed from both brain hemispheres and shared similarities with somatosensory evoked potentials (SSEP) from median nerve stimulation. Use of a 0.5 ms pulse duration (PD) sonication given at 70% duty cycle, compared to the use of 1 and 2 ms PD, elicited more distinctive FEP peak features from the hemisphere ipsilateral to sonication. Although several participants reported hearing tones associated with FUS stimulation, the observed FEP were not likely to be confounded by the auditory sensation based on a separate measurement of auditory evoked potentials (AEP) to tonal stimulation (mimicking the same repetition frequency as the FUS stimulation). Off-line changes in resting-state functional connectivity (FC) associated with thalamic stimulation revealed that the FUS stimulation enhanced connectivity in a network of sensorimotor and sensory integration areas, which lasted for at least more than an hour. Clinical neurological evaluations, EEG, and neuroanatomical MRI did not reveal any adverse or unintended effects of sonication, attesting its safety. These results suggest that FUS stimulation may induce long-term neuroplasticity in humans, indicating its neurotherapeutic potential for various neurological and neuropsychiatric conditions.

Concurrent behavioral and electrophysiological longitudinal recordings for in vivo assessment of aging

Electrophysiological and behavioral alterations, including sleep and cognitive impairments, are critical components of age-related decline and neurodegenerative diseases. In preclinical investigation, many refined techniques are employed to probe these phenotypes, but they are often conducted separately. Herein, we provide a protocol for one-time surgical implantation of EMG wires in the nuchal muscle and a skull-surface EEG headcap in mice, capable of 9-to-12-month recording longevity. All data acquisitions are wireless, making them compatible with simultaneous EEG recording coupled to multiple behavioral tasks, as we demonstrate with locomotion/sleep staging during home-cage video assessments, cognitive testing in the Barnes maze, and sleep disruption. Time-course EEG and EMG data can be accurately mapped to the behavioral phenotype and synchronized with neuronal frequencies for movement and the location to target in the Barnes maze. We discuss critical steps for optimizing headcap surgery and alternative approaches, including increasing the number of EEG channels or utilizing depth electrodes with the system. Combining electrophysiological and behavioral measurements in preclinical models of aging and neurodegeneration has great potential for improving mechanistic and therapeutic assessments and determining early markers of brain disorders.

A review of MEG dynamic brain network research

The dynamic description of neural networks has attracted the attention of researchers for dynamic networks may carry more information compared with resting-state networks. As a non-invasive electrophysiological data with high temporal and spatial resolution, magnetoencephalogram (MEG) can provide rich information for the analysis of dynamic functional brain networks. In this review, the development of MEG brain network was summarized. Several analysis methods such as sliding window, Hidden Markov model, and time-frequency based methods used in MEG dynamic brain network studies were discussed. Finally, the current research about multi-modal brain network analysis and their applications with MEG neurophysiology, which are prospected to be one of the research directions in the future, were concluded.

Insights of neurophysiology on unconscious state using combined transcranial magnetic stimulation and electroencephalography: A systematic review

Unconscious state has been investigated in numerous studies so far, but pathophysiology of this state is not fully understood. Recently, combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) has been developed to allow for non-invasive assessment of neurophysiology in the cerebral cortex. We conducted a systematic literature search for TMS-EEG studies on human unconscious state using PubMed with cross-reference and manual searches. The initial search yielded 137 articles, and 19 of them were identified as relevant, including one article found by manual search. This review included 10 studies for unresponsive wakefulness syndrome (UWS), 9 for minimally conscious states (MCS), 5 for medication-induced unconscious states, and 6 for natural non-rapid eye movement states. These studies analyzed TMS-evoked potential to calculate perturbational complexity index (PCI) and OFF-periods. In particular, PCI was found to be a potentially useful marker to differentiate between UWS and MCS. This review demonstrated that TMS-EEG could represent a promising neuroscientific tool to investigate various unconscious states. Further TMS-EEG research may help elucidate the neural basis of unconscious state.

Objective assessment of impulse control disorder in patients with Parkinson's disease using a low-cost LEGO-like EEG headset: a feasibility study

Background

Patients with Parkinson’s disease (PD) can develop impulse control disorders (ICDs) while undergoing a pharmacological treatment for motor control dysfunctions with a dopamine agonist (DA). Conventional clinical interviews or questionnaires can be biased and may not accurately diagnose at the early stage. A wearable electroencephalogram (EEG)-sensing headset paired with an examination procedure can be a potential user-friendly method to explore ICD-related signatures that can detect its early signs and progression by reflecting brain activity.

Methods

A stereotypical Go/NoGo test that targets impulse inhibition was performed on 59 individuals, including healthy controls, patients with PD, and patients with PD diagnosed by ICDs. We conducted two Go/NoGo sessions before and after the DA-pharmacological treatment for the PD and ICD groups. A low-cost LEGO-like EEG headset was used to record concurrent EEG signals. Then, we used the event-related potential (ERP) analytical framework to explore ICD-related EEG abnormalities after DA treatment.

Results

After the DA treatment, only the ICD-diagnosed PD patients made more behavioral errors and tended to exhibit the deterioration for the NoGo N2 and P3 peak amplitudes at fronto-central electrodes in contrast to the HC and PD groups. Particularly, the extent of the diminished NoGo-N2 amplitude was prone to be modulated by the ICD scores at Fz with marginal statistical significance (r = − 0.34, p = 0.07).

Conclusions

The low-cost LEGO-like EEG headset successfully captured ERP waveforms and objectively assessed ICD in patients with PD undergoing DA treatment. This objective neuro-evidence could provide complementary information to conventional clinical scales used to diagnose ICD adverse effects.

Spatial Covariance of Cholinergic Muscarinic M1 /M4 Receptors in Parkinson's Disease

BACKGROUND

Parkinson's disease (PD) is associated with cholinergic dysfunction, although the role of M1 and M4 receptors remains unclear.

OBJECTIVE

To investigate spatial covariance patterns of cholinergic muscarinic M₁ /M₄ receptors in PD and their relationship with cognition and motor symptoms.

METHODS

Some 19 PD and 24 older adult controls underwent ¹²³ I-iodo-quinuclidinyl-benzilate (QNB) (M₁ /M₄ receptor) and ^99m Tc-exametazime (perfusion) single-photon emission computed tomography (SPECT) scanning. We implemented voxel principal components analysis, producing a series of images representing patterns of intercorrelated voxels across individuals. Linear regression analyses derived specific M₁ /M₄ spatial covariance patterns associated with PD.

RESULTS

A cholinergic M₁ /M₄ pattern that converged onto key hubs of the default, auditory-visual, salience, and sensorimotor networks fully discriminated PD patients from controls (F_1,41  = 135.4, P < 0.001). In PD, we derived M₁ /M₄ patterns that correlated with global cognition (r = -0.62, P = 0.008) and motor severity (r = 0.53, P = 0.02). Both patterns emerged with a shared topography implicating the basal forebrain as well as visual, frontal executive, and salience circuits. Further, we found a M₁ /M₄ pattern that predicted global cognitive decline (r = 0.46, P = 0.04) comprising relative decreased binding within default and frontal executive networks.

CONCLUSIONS

Cholinergic muscarinic M₁ /M₄ modulation within key brain networks were apparent in PD. Cognition and motor severity were associated with a similar topography, inferring both phenotypes possibly rely on related cholinergic mechanisms. Relative decreased M₁ /M₄ binding within default and frontal executive networks could be an indicator of future cognitive decline. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Cognitive dysfunction in amyotrophic lateral sclerosis: can we predict it?

Background and aim

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by the degeneration of both upper and lower motoneurons in the brain and spinal cord leading to motor and extra-motor symptoms. Although traditionally considered a pure motor disease, recent evidences suggest that ALS is a multisystem disorder. Neuropsychological alterations, in fact, are observed in more than 50% of patients: while executive dysfunctions have been firstly identified, alterations in verbal fluency, behavior, and pragmatic and social cognition have also been described. Detecting and monitoring ALS cognitive and behavioral impairment even at early disease stages is likely to have staging and prognostic implications, and it may impact the enrollment in future clinical trials. During the last 10 years, humoral, radiological, neurophysiological, and genetic biomarkers have been reported in ALS, and some of them seem to potentially correlate to cognitive and behavioral impairment of patients. In this review, we sought to give an up-to-date state of the art of neuropsychological alterations in ALS: we will describe tests used to detect cognitive and behavioral impairment, and we will focus on promising non-invasive biomarkers to detect pre-clinical cognitive decline.

Conclusions

To date, the research on humoral, radiological, neurophysiological, and genetic correlates of neuropsychological alterations is at the early stage, and no conclusive longitudinal data have been published. Further and longitudinal studies on easily accessible and quantifiable biomarkers are needed to clarify the time course and the evolution of cognitive and behavioral impairments of ALS patients.

Cognitive network hyperactivation and motor cortex decline correlate with ALS prognosis

We aimed to quantitatively characterize progressive brain network disruption in Amyotrophic Lateral Sclerosis (ALS) during cognition using the mismatch negativity (MMN), an electrophysiological index of attention switching. We measured the MMN using 128-channel EEG longitudinally (2-5 timepoints) in 60 ALS patients and cross-sectionally in 62 healthy controls. Using dipole fitting and linearly constrained minimum variance beamforming we investigated cortical source activity changes over time. In ALS, the inferior frontal gyri (IFG) show significantly lower baseline activity compared to controls. The right IFG and both superior temporal gyri (STG) become progressively hyperactive longitudinally. By contrast, the left motor and dorsolateral prefrontal cortices are initially hyperactive, declining progressively. Baseline motor hyperactivity correlates with cognitive disinhibition, and lower baseline IFG activities correlate with motor decline rate, while left dorsolateral prefrontal activity predicted cognitive and behavioural impairment. Shorter survival correlates with reduced baseline IFG and STG activity and later STG hyperactivation. Source-resolved EEG facilitates quantitative characterization of symptom-associated and symptom-preceding motor and cognitive-behavioral cortical network decline in ALS.

The imaging signature of C9orf72 hexanucleotide repeat expansions: implications for clinical trials and therapy development

While C9orf72-specific imaging signatures have been proposed by both ALS and FTD research groups and considerable presymptomatic alterations have also been confirmed in young mutation carriers, considerable inconsistencies exist in the literature. Accordingly, a systematic review of C9orf72-imaging studies has been performed to identify consensus findings, stereotyped shortcomings, and unique contributions to outline future directions. A formal literature review was conducted according to the STROBE guidelines. All identified papers were individually reviewed for sample size, choice of controls, study design, imaging modalities, statistical models, clinical profiling, and identified genotype-associated pathological patterns. A total of 74 imaging papers were systematically reviewed. ALS patients with GGGGCC repeat expansions exhibit relatively limited motor cortex involvement and widespread extra-motor pathology. C9orf72 positive FTD patients often show preferential posterior involvement. Reports of thalamic involvement are relatively consistent across the various phenotypes. Asymptomatic hexanucleotide repeat carriers often exhibit structural and functional changes decades prior to symptom onset. Common shortcomings included sample size limitations, lack of disease-controls, limited clinical profiling, lack of genetic testing in healthy controls, and absence of post mortem validation. There is a striking paucity of longitudinal studies and existing presymptomatic studies have not evaluated the predictive value of radiological changes with regard to age of onset and phenoconversion. With the advent of antisense oligonucleotide therapies, the meticulous characterisation of C9orf72-associated changes has gained practical relevance. Neuroimaging offers non-invasive biomarkers for future clinical trials, presymptomatic ascertainment, diagnostic and prognostic applications.

Frontal functional connectivity and disease duration interactively predict cognitive decline in Parkinson's disease

OBJECTIVE

Cognitive decline does not always follow a predictable course in Parkinson's disease (PD), with some patients remaining stable while others meet criteria for dementia from early stages. Functional connectivity has been proposed as a good correlate of cognitive decline in PD, although it has not been explored whether the association between this connectivity and cognitive ability is influenced by disease duration, which was our objective.

METHODS

We included 30 patients with PD and 15 healthy controls (HC). Six cognitive domains were estimated based on neuropsychological assessment. Phase-based connectivity at frontal and posterior cortical regions was estimated from a resting EEG.

RESULTS

The PD group showed significant impairment for the executive, visuospatial, and language domains compared with HC. Increased connectivity at frontal regions was also found in the PD group. Frontal delta and theta connectivity negatively influenced general cognition and visuospatial performance, but this association was moderated by disease duration, with increased connectivity predicting worse performance after 8 years of disease duration.

CONCLUSION

Subtle neurophysiological changes underlie cognitive decline along PD progression, especially around a decade after motor symptoms onset.

SIGNIFICANCE

Connectivity of EEG slow waves at frontal regions might be used as a predictor of cognitive decline in PD.

Frontal Functional Network Disruption Associated with Amyotrophic Lateral Sclerosis: An fNIRS-Based Minimum Spanning Tree Analysis

Recent evidence increasingly associates network disruption in brain organization with multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), a rare terminal disease. However, the comparability of brain network characteristics across different studies remains a challenge for conventional graph theoretical methods. One suggested method to address this issue is minimum spanning tree (MST) analysis, which provides a less biased comparison. Here, we assessed the novel application of MST network analysis to hemodynamic responses recorded by functional near-infrared spectroscopy (fNIRS) neuroimaging modality, during an activity-based paradigm to investigate hypothetical disruptions in frontal functional brain network topology as a marker of the executive dysfunction, one of the most prevalent cognitive deficit reported across ALS studies. We analyzed data recorded from nine participants with ALS and ten age-matched healthy controls by first estimating functional connectivity, using phase-locking value (PLV) analysis, and then constructing the corresponding individual and group MSTs. Our results showed significant between-group differences in several MST topological properties, including leaf fraction, maximum degree, diameter, eccentricity, and degree divergence. We further observed a global shift toward more centralized frontal network organizations in the ALS group, interpreted as a more random or dysregulated network in this cohort. Moreover, the similarity analysis demonstrated marginally significantly increased overlap in the individual MSTs from the control group, implying a reference network with lower topological variation in the healthy cohort. Our nodal analysis characterized the main local hubs in healthy controls as distributed more evenly over the frontal cortex, with slightly higher occurrence in the left prefrontal cortex (PFC), while in the ALS group, the most frequent hubs were asymmetrical, observed primarily in the right prefrontal cortex. Furthermore, it was demonstrated that the global PLV (gPLV) synchronization metric is associated with disease progression, and a few topological properties, including leaf fraction and tree hierarchy, are linked to disease duration. These results suggest that dysregulation, centralization, and asymmetry of the hemodynamic-based frontal functional network during activity are potential neuro-topological markers of ALS pathogenesis. Our findings can possibly support new bedside assessments of the functional status of ALS' brain network and could hypothetically extend to applications in other neurodegenerative diseases.

Structure and function of the perivascular fluid compartment and vertebral venous plexus: Illumining a novel theory on mechanisms underlying the pathogenesis of Alzheimer's, cerebral small vessel, and neurodegenerative diseases

Blood dynamically and richly supplies the cerebral tissue via microvessels invested in pia matter perforating the cerebral substance. Arteries penetrating the cerebral substance derive an investment from one or two successive layers of pia mater, luminally apposed to the pial-glial basal lamina of the microvasculature and abluminally apposed to a series of aquaporin IV-studded astrocytic end feet constituting the soi-disant glia limitans. The full investment of successive layers forms the variably continuous walls of the periarteriolar, pericapillary, and perivenular divisions of the perivascular fluid compartment. The pia matter disappears at the distal periarteriolar division of the perivascular fluid compartment. Plasma from arteriolar blood sequentially transudates into the periarteriolar division of the perivascular fluid compartment and subarachnoid cisterns in precession to trickling into the neural interstitium. Fluid from the neural interstitium successively propagates into the venules through the subarachnoid cisterns and perivenular division of the perivascular fluid compartment. Fluid fluent within the perivascular fluid compartment flows gegen the net direction of arteriovenular flow. Microvessel oscillations at the central tendency of the cerebral vasomotion generate corresponding oscillations of within the surrounding perivascular fluid compartment, interposed betwixt the abluminal surface of the vessels and internal surface of the pia mater. The precise microanatomy of this most fascinating among designable spaces has eluded the efforts of various investigators to interrogate its structure, though most authors non-consensusly concur the investing layers effectively and functionally segregate the perivascular and subarachnoid fluid compartments. Enlargement of the perivascular fluid compartment in a variety of neurological disorders, including senile dementia of the Alzheimer's type and cerebral small vessel disease, may alternately or coordinately constitute a correlative marker of disease severity and a possible cause implicated in the mechanistic pathogenesis of these conditions. Venular pressures modulating oscillatory dynamic flow within the perivascular fluid compartment may similarly contribute to the development of a variety among neurological disorders. An intimate understanding of subtle features typifying microanatomy and microphysiology of the investing structures and spaces of the cerebral microvasculature may powerfully inform mechanistic pathophysiology mediating a variety of neurovascular ischemic, neuroinfectious, neuroautoimmune, and neurodegenerative diseases.

Dietary Melatonin Therapy Alleviates the Lamina Cribrosa Damages in Patients with Mild Cognitive Impairments: A Double-Blinded, Randomized Controlled Study

BACKGROUND Alzheimer's disease (AD) is a degenerative disease that is characterized by massive neuron devastations in the hippocampus and cortex. Mild cognitive impairment (MCI) is the transitory stage between normality and AD dementia. This study aimed to investigate the melatonin induced effects on the lamina cribrosa thickness (LCT) of patients with MCI. MATERIAL AND METHODS The LCT data of patients with MCI were compared to LCT data of healthy controls. Subsequently, all MCI patients were randomly assigned into an experimental group (with melatonin treatment) or a placebo group (without any melatonin treatment). RESULTS The LCT of MCI patients decreased significantly compared with healthy controls. The univariate analysis showed that the lower the Mini Mental State Examination (MMSE) score (P=0.038; 95% CI: 0.876, -0.209), the smaller hippocampus volume (P=0.001; 95% CI: -1.594, -2.911), and the upregulated level of cerebrospinal fluid (CSF) T-tau (P=0.036; 95% CI: 2.546, -0.271) were associated significantly with the thinner LCT in MCI patients. There were 40 patients in the experimental group and 39 patients in the placebo group. The mean age of the experimental group was not significantly different from the placebo group (66.3±8.8 versus 66.5±8.3; P>0.05). The LCT and hippocampus volume of the melatonin treated group were significantly larger compared with the placebo group (P<0.001). On the other hand, the CSF T-tau level of the melatonin treated group was significantly lower compared with the untreated group (P<0.001). CONCLUSIONS LCT assessment might allow early diagnosis of MCI. Dietary melatonin therapy could provide an effective medication for MCI patients with LCT alterations.

Localization of Brain Networks Engaged by the Sustained Attention to Response Task Provides Quantitative Markers of Executive Impairment in Amyotrophic Lateral Sclerosis

Objective: To identify cortical regions engaged during the sustained attention to response task (SART) and characterize changes in their activity associated with the neurodegenerative condition amyotrophic lateral sclerosis (ALS). Methods: High-density electroencephalography (EEG) was recorded from 33 controls and 23 ALS patients during a SART paradigm. Differences in associated event-related potential peaks were measured for Go and NoGo trials. Sources active during these peaks were localized, and ALS-associated differences were quantified. Results: Go and NoGo N2 and P3 peak sources were localized to the left primary motor cortex, bilateral dorsolateral prefrontal cortex (DLPFC), and lateral posterior parietal cortex (PPC). NoGo trials evoked greater bilateral medial PPC activity during N2 and lesser left insular, PPC and DLPFC activity during P3. Widespread cortical hyperactivity was identified in ALS during P3. Changes in the inferior parietal lobule and insular activity provided very good discrimination (AUROC > 0.75) between patients and controls. Activation of the right precuneus during P3 related to greater executive function in ALS, indicative of a compensatory role. Interpretation: The SART engages numerous frontal and parietal cortical structures. SART–EEG measures correlate with specific cognitive impairments that can be localized to specific structures, aiding in differential diagnosis.

Patterned functional network disruption in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease primarily affecting motor function, with additional evidence of extensive nonmotor involvement. Despite increasing recognition of the disease as a multisystem network disorder characterised by impaired connectivity, the precise neuroelectric characteristics of impaired cortical communication remain to be fully elucidated. Here, we characterise changes in functional connectivity using beamformer source analysis on resting‐state electroencephalography recordings from 74 ALS patients and 47 age‐matched healthy controls. Spatiospectral characteristics of network changes in the ALS patient group were quantified by spectral power, amplitude envelope correlation (co‐modulation) and imaginary coherence (synchrony). We show patterns of decreased spectral power in the occipital and temporal (δ‐ to β‐band), lateral/orbitofrontal (δ‐ to θ‐band) and sensorimotor (β‐band) regions of the brain in patients with ALS. Furthermore, we show increased co‐modulation of neural oscillations in the central and posterior (δ‐, θ‐ and γ_l‐band) and frontal (δ‐ and γ_l‐band) regions, as well as decreased synchrony in the temporal and frontal (δ‐ to β‐band) and sensorimotor (β‐band) regions. Factorisation of these complex connectivity patterns reveals a distinct disruption of both motor and nonmotor networks. The observed changes in connectivity correlated with structural MRI changes, functional motor scores and cognitive scores. Characteristic patterned changes of cortical function in ALS signify widespread disease‐associated network disruption, pointing to extensive dysfunction of both motor and cognitive networks. These statistically robust findings, that correlate with clinical scores, provide a strong rationale for further development as biomarkers of network disruption for future clinical trials.