Molecular nexopathies: a new paradigm of neurodegenerative disease

Disruption of macroscale functional network organisation in patients with frontotemporal dementia

Neurodegenerative dementias have a profound impact on higher-order cognitive and behavioural functions. Investigating macroscale functional networks through cortical gradients provides valuable insights into the neurodegenerative dementia process and overall brain function. This approach allows for the exploration of unimodal-multimodal differentiation and the intricate interplay between functional brain networks. We applied cortical gradients mapping to resting-state functional MRI data of patients with frontotemporal dementia (FTD) (behavioural-bvFTD, non-fluent and semantic) and healthy controls. In healthy controls, the principal gradient maximally distinguished sensorimotor from default-mode network (DMN) and the secondary gradient visual from salience network (SN). In all FTD variants, the principal gradient's unimodal-multimodal differentiation was disrupted. The secondary gradient, however, showed widespread disruptions impacting the interactions among all networks specifically in bvFTD, while semantic and non-fluent variants exhibited more focal alterations in limbic and sensorimotor networks. Additionally, the visual network showed responsive and/or compensatory changes in all patients. Importantly, these disruptions extended beyond atrophy distribution and related to symptomatology in patients with bvFTD. In conclusion, optimal brain function requires networks to operate in a segregated yet collaborative manner. In FTD, our findings indicate a collapse and loss of differentiation between networks not solely explained by atrophy. These specific cortical gradients' fingerprints could serve as a functional signature for identifying early changes in neurodegenerative diseases or potential compensatory processes.

Convergent large-scale network and local vulnerabilities underlie brain atrophy across Parkinson's disease stages: a worldwide ENIGMA study

Parkinson's disease (PD) is associated with extensive structural brain changes. Recent work has proposed that the spatial pattern of disease pathology is shaped by both network spread and local vulnerability. However, only few studies assessed these biological frameworks in large patient samples across disease stages. Analyzing the largest imaging cohort in PD to date (N = 3,096 patients), we investigated the roles of network architecture and local brain features by relating regional abnormality maps to normative profiles of connectivity, intrinsic networks, cytoarchitectonics, neurotransmitter receptor densities, and gene expression. We found widespread cortical and subcortical atrophy in PD to be associated with advancing disease stage, longer time since diagnosis, and poorer global cognition. Structural brain connectivity best explained cortical atrophy patterns in PD and across disease stages. These patterns were robust among individual patients. The precuneus, lateral temporal cortex, and amygdala were identified as likely network-based epicentres, with high convergence across disease stages. Individual epicentres varied significantly among patients, yet they consistently localized to the default mode and limbic networks. Furthermore, we showed that regional overexpression of genes implicated in synaptic structure and signalling conferred increased susceptibility to brain atrophy in PD. In summary, this study demonstrates in a well-powered sample that structural brain abnormalities in PD across disease stages and within individual patients are influenced by both network spread and local vulnerability.

Deep learning for Parkinson's disease classification using multimodal and multi-sequences PET/MR images

BACKGROUND

We aimed to use deep learning (DL) techniques to accurately differentiate Parkinson's disease (PD) from multiple system atrophy (MSA), which share similar clinical presentations. In this retrospective analysis, 206 patients who underwent PET/MR imaging at the Chinese PLA General Hospital were included, having been clinically diagnosed with either PD or MSA; an additional 38 healthy volunteers served as normal controls (NC). All subjects were randomly assigned to the training and test sets at a ratio of 7:3. The input to the model consists of 10 two-dimensional (2D) slices in axial, coronal, and sagittal planes from multi-modal images. A modified Residual Block Network with 18 layers (ResNet18) was trained with different modal images, to classify PD, MSA, and NC. A four-fold cross-validation method was applied in the training set. Performance evaluations included accuracy, precision, recall, F1 score, Receiver operating characteristic (ROC), and area under the ROC curve (AUC).

RESULTS

Six single-modal models and seven multi-modal models were trained and tested. The PET models outperformed MRI models. The 11C-methyl-N-2β-carbomethoxy-3β-(4-fluorophenyl)-tropanel (11C-CFT) -Apparent Diffusion Coefficient (ADC) model showed the best classification, which resulted in 0.97 accuracy, 0.93 precision, 0.95 recall, 0.92 F1, and 0.96 AUC. In the test set, the accuracy, precision, recall, and F1 score of the CFT-ADC model were 0.70, 0.73, 0.93, and 0.82, respectively.

CONCLUSIONS

The proposed DL method shows potential as a high-performance assisting tool for the accurate diagnosis of PD and MSA. A multi-modal and multi-sequence model could further enhance the ability to classify PD.

Modulatory effect of levodopa on the basal ganglia-cerebellum connectivity in Parkinson's disease

Long-term levodopa use in Parkinson's disease is associated with declining efficacy and motor complications. Understanding its effects on brain reorganisation is vital for optimizing therapy. Using data from Parkinson's Progression Marker Initiative, we investigated levodopa's impact on resting-state functional connectivity within the cortico-basal ganglia-cerebellum system in 29 patients, under drug-naïve and levodopa-medicated conditions. Univariate comparisons of inter-regional connectivity between conditions were conducted, and multivariate combinations of connections within and between networks were assessed. No significant effect of levodopa was found using the univariate seed-based approach. However, the network connectivity pattern between basal ganglia and cerebellum showed robust classification power. Eigenvector Centrality Mapping (ECM) further identified functional hubs, with cerebellar hubs being the only ones within the cortico-basal ganglia-cerebellum system affected by medication. Our study provides further insight into the importance of inter-network functional connectivity in Parkinson's and the impact of levodopa medication, highlighting the often-overlooked role of the cerebellum.

Directionality bias underpins divergent spatiotemporal progression of Alzheimer-related tauopathy in mouse models

INTRODUCTION

Trans-synaptic connectome-based spread is a shared mechanism behind different tauopathic conditions, but they exhibit divergent spatiotemporal progression. One explanation is that conditions may incur directional biases in tau transmission along fiber tracts.

METHODS

We examined this hypothesis using tau data from 11 distinct mouse models across four experimental studies. For this purpose, we extended a network-based spread model by incorporating net directionality along the connectome.

RESULTS

Retrograde bias better predicted tau progression than anterograde bias, but our best-fitting biophysical models incorporate the mixed effects of both retrograde- and anterograde-directed spread, with notable tau-strain-specific differences. There was a nontrivial association between directionality bias and tau aggressiveness, with more virulent strains exhibiting less retrograde character.

DISCUSSION

Our study implicates directional bias in tau transmission along fiber tracts as a general feature of tauopathy spread and a strong candidate for explaining for the diversity of spatiotemporal tau progression between conditions.

HIGHLIGHTS

Connectome-based spread is a feature underpinning tauopathic diseases, including Alzheimer's Eleven mouse models of tauopathy across four studies were explored Mathematical models of retrograde and nondirectional spread performed better than anterograde Different mouse models of tauopathy exhibited distinct spread biases Retrograde-biased spread tended to be associated with less aggressive tau strains.

Heterogeneous tau deposition patterns in the preclinical stage link to domain-specific cognitive deficits

INTRODUCTION

The spatial heterogeneity of tau deposition is closely linked to clinical variants of Alzheimer's disease (AD). Detecting these patterns in the preclinical stage is challenging, but second-generation tau tracers provide a unique opportunity to do so.

METHODS

We used independent component analysis (ICA) and tau positron emission tomography (PET) imaging with the 18F-MK6240 tracer in 590 cognitively healthy adults (mean age 66.58 ± 5.13 years, 340 females) to identify tau patterns in the preclinical stage.

RESULTS

Using all individuals, seven distinct patterns emerged, with medial temporal lobe (MTL) involvement associated with age, Aβ burden, apolipoprotein E (APOE) genotype, and plasma total tau. Bilateral amygdala-hippocampus tau deposition was associated negatively with memory (t = -2.64, p < 0.01), while broader neocortical patterns, especially asymmetric ones, were linked to deficits in language (t < -3.13, p < 0.002) and reasoning (t < -2.63, p < 0.01).

DISCUSSION

These findings advance our understanding of preclinical tau heterogeneity, offering new insights for early AD intervention.

HIGHLIGHTS

Seven tau deposition patterns were identified in preclinical stages of AD, including medial temporal lobe and asymmetric neocortical patterns. Medial temporal lobe patterns were strongly linked to age, APOE genotype, Aβ burden, and plasma total tau levels. Neocortical patterns, especially asymmetric ones, were linked to domain-specific cognitive deficits, notably in language and reasoning. This research highlights the potential of using tau deposition patterns for early detection and tailoring interventions in preclinical AD.

Inter-network functional connectivity increases by beta-amyloid and may facilitate the early stage of tau accumulation

Alzheimer's disease (AD) is pathologically marked by tau tangles and beta-amyloid (Aβ) plaques. It has been hypothesized that Aβ facilitates spread of tau outside of the medial temporal lobe (MTL), but exact mechanism of this facilitation remains unclear. We aimed to test the hypothesis that abnormal Aβ induces an increase in inter-network functional connectivity, which in turn induces early-stage tau elevation in limbic network. Our study used 18F-Florbetaben Aβ positron emission tomography (PET), 18F-MK6240 tau-PET, and resting-state functional magnetic resonance imaging (rs-fMRI) from 489 healthy unimpaired older adults, including 46 with longitudinal data. We found significant correlations between tau in limbic network and Aβ in distinct functional networks. We then demonstrated that Aβ+ /Tau- participants exhibited elevated inter-network functional connectivity of the limbic network. Finally, our longitudinal results showed that annual increases in inter-network functional connectivity between limbic network and default mode and control networks were linked to annual tau elevation in limbic network, primarily modulated by Aβ+ individuals. Understanding this early brain alteration in response to pathologies could guide treatments early in disease course.

Protocol for evaluating neuronal activity and neurotransmitter release following amyloid-beta oligomer injections into the rat hippocampus

In Alzheimer's disease, there is an imbalance in neurotransmitter release and altered neuronal activation. Here, we present a protocol approach to analyze neuronal activity by combining local field potential (LFP) recording with microdialysis within the same animal. We describe steps for measuring glutamate and GABA levels following hippocampal amyloid-beta oligomer (Aβo) injections in rats. We then detail procedures for assembling the electrode and cannula, surgical implantation and simultaneous in vivo LFP recording, interstitial fluid collection, and Aβo injections.

Predicting Parkinson's disease trajectory using clinical and functional MRI features: A reproduction and replication study

Parkinson's disease (PD) is a common neurodegenerative disorder with a poorly understood physiopathology and no established biomarkers for the diagnosis of early stages and for prediction of disease progression. Several neuroimaging biomarkers have been studied recently, but these are susceptible to several sources of variability related for instance to cohort selection or image analysis. In this context, an evaluation of the robustness of such biomarkers to variations in the data processing workflow is essential. This study is part of a larger project investigating the replicability of potential neuroimaging biomarkers of PD. Here, we attempt to fully reproduce (reimplementing the experiments with the same methods, including data collection from the same database) and replicate (different data and/or method) the models described in (Nguyen et al., 2021) to predict individual's PD current state and progression using demographic, clinical and neuroimaging features (fALFF and ReHo extracted from resting-state fMRI). We use the Parkinson's Progression Markers Initiative dataset (PPMI, ppmi-info.org), as in (Nguyen et al., 2021) and aim to reproduce the original cohort, imaging features and machine learning models as closely as possible using the information available in the paper and the code. We also investigated methodological variations in cohort selection, feature extraction pipelines and sets of input features. Different criteria were used to evaluate the reproduction attempt and compare the results with the original ones. Notably, we obtained significantly better than chance performance using the analysis pipeline closest to that in the original study (R2 > 0), which is consistent with its findings. In addition, we performed a partial reproduction using derived data provided by the authors of the original study, and we obtained results that were close to the original ones. The challenges encountered while attempting to reproduce (fully and partially) and replicating the original work are likely explained by the complexity of neuroimaging studies, in particular in clinical settings. We provide recommendations to further facilitate the reproducibility of such studies in the future.

Common neuroanatomical differential factors underlying heterogeneous gray matter volume variations in five common psychiatric disorders

Multifaceted evidence has shown that psychiatric disorders share common neurobiological mechanisms. However, the tremendous inter-individual heterogeneity among patients with psychiatric disorders limits trans-diagnostic studies with case-control designs, aimed at identifying clinically promising neuroimaging biomarkers. This study aims to identify neuroanatomical differential factors (ND factors) underlying gray matter volume variations in five psychiatric disorders. We leverage 4 independent datasets of 878 patients diagnosed with psychiatric disorders and 585 healthy controls (HCs) to identify shared ND factors underlying individualized gray matter volume variations. Individualized gray matter volume variations are represented with the linear weighted sum of ND factors, and each case is assigned a unique factor composition, thus preserving interindividual variation. We identify four robust ND factors that can be generalized to unseen disorders. ND factors show significant association with group-level morphological abnormalities, reconciling individual- and group-level morphological abnormalities, and are characterized by dissociable cognitive processes, molecular signatures, and connectome-informed epicenters. Moreover, using factor compositions as features, we discover two robust transdiagnostic subtypes with opposite gray matter volume variations relative to HCs. In conclusion, we identify four reproducible and shared neuroanatomical factors that underlie the highly heterogeneous morphological abnormalities in psychiatric disorders.

The graded multidimensional geometry of phenotypic variation and progression in neurodegenerative syndromes

Clinical variants of Alzheimer's disease and frontotemporal lobar degeneration display a spectrum of cognitive-behavioural changes varying between individuals and over time. Understanding the landscape of these graded individual/group level longitudinal variations is critical for precise phenotyping; however, this remains challenging to model. Addressing this challenge, we leverage the National Alzheimer's Coordinating Center database to derive a unified geometric framework of graded longitudinal phenotypic variation in Alzheimer's disease and frontotemporal lobar degeneration. We included three time point, cognitive-behavioural and clinical data from 390 typical, atypical and intermediate Alzheimer's disease and frontotemporal lobar degeneration variants (114 typical Alzheimer's disease; 107 behavioural variant frontotemporal dementia; 42 motor variants of frontotemporal lobar degeneration; and 103 primary progressive aphasia patients). On these data, we applied advanced data-science approaches to derive low-dimensional geometric spaces capturing core features underpinning clinical progression of Alzheimer's disease and frontotemporal lobar degeneration syndromes. To do so, we first used principal component analysis to derive six axes of graded longitudinal phenotypic variation capturing patient-specific movement along and across these axes. Then, we distilled these axes into a visualizable 2D manifold of longitudinal phenotypic variation using Uniform Manifold Approximation and Projection. Both geometries together enabled the assimilation and interrelation of paradigmatic and mixed cases, capturing dynamic individual trajectories and linking syndromic variability to neuropathology and key clinical end points, such as survival. Through these low-dimensional geometries, we show that (i) specific syndromes (Alzheimer's disease and primary progressive aphasia) converge over time into a de-differentiated pooled phenotype, while others (frontotemporal dementia variants) diverge to look different from this generic phenotype; (ii) phenotypic diversification is predicted by simultaneous progression along multiple axes, varying in a graded manner between individuals and syndromes; and (iii) movement along specific principal axes predicts survival at 36 months in a syndrome-specific manner and in individual pathological groupings. The resultant mapping of dynamics underlying cognitive-behavioural evolution potentially holds paradigm-changing implications to predicting phenotypic diversification and phenotype-neurobiological mapping in Alzheimer's disease and frontotemporal lobar degeneration.

Network Diffusion-Constrained Variational Generative Models for Investigating the Molecular Dynamics of Brain Connectomes Under Neurodegeneration

Alzheimer's disease (AD) is a complex and progressive neurodegenerative condition with significant societal impact. Understanding the temporal dynamics of its pathology is essential for advancing therapeutic interventions. Empirical and anatomical evidence indicates that network decoupling occurs as a result of gray matter atrophy. However, the scarcity of longitudinal clinical data presents challenges for computer-based simulations. To address this, a first-principles-based, physics-constrained Bayesian framework is proposed to model time-dependent connectome dynamics during neurodegeneration. This temporal diffusion network framework segments pathological progression into discrete time windows and optimizes connectome distributions for biomarker Bayesian regression, conceptualized as a learning problem. The framework employs a variational autoencoder-like architecture with computational enhancements to stabilize and improve training efficiency. Experimental evaluations demonstrate that the proposed temporal meta-models outperform traditional static diffusion models. The models were evaluated using both synthetic and real-world MRI and PET clinical datasets that measure amyloid beta, tau, and glucose metabolism. The framework successfully distinguishes normative aging from AD pathology. Findings provide novel support for the "decoupling" hypothesis and reveal eigenvalue-based evidence of pathological destabilization in AD. Future optimization of the model, integrated with real-world clinical data, is expected to improve applications in personalized medicine for AD and other neurodegenerative diseases.

Striatal-cortical dysconnectivity underlies somatosensory deficits in Parkinson's disease: Insights from rhythmic auditory-motor training

Evidence indicates that neurodegenerative diseases spread through distinct brain networks. For Parkinson's disease (PD), somatosensory abnormalities may accompany motor dysfunction in early disease stages when dopaminergic degeneration is limited to the basal ganglia. It remains unclear whether, based on the network-spread account, these abnormalities emanated from aberrant functional connectivity with the basal ganglia, and whether interventions normalizing this connectivity could reverse these abnormalities. Here, we employed functional MRI to record brain responses to tactile stimuli in patients with idiopathic PD and healthy controls before and after three-week rhythmic auditory stimulation-assisted gait (RASg) training. Consistent with the presence of striatal degeneration, patients showed right posterior putamen (pPut) hypoactivation when detecting tactile stimuli of their left leg. They also exhibited reduced functional connectivity from the right pPut to the right parietal somatosensory region (inferior parietal lobule, IPL), whose hypoactivation reflected patients' impaired tactile detectability. Importantly, this dysconnectivity predicted right IPL hypoactivation, indicating that pPut-IPL dysconnectivity underlay patients' impaired tactile detectability. Intriguingly, RASg training normalized patients' tactile detectability, which was mirrored by normalization of right IPL activation and pPut-IPL connectivity. Training-induced changes in pPut-IPL connectivity predicted changes in IPL activation during tactile detection, reinforcing the role of pPut-IPL connectivity in patients' tactile detectability. These findings suggest that somatosensory abnormalities in PD may arise from the spread of striatal pathology to relevant cortical regions. Rhythmic auditory-motor training acts to recover striatal connectivity, improving PD patients' somatosensory deficits.

Can brain network connectivity facilitate the clinical development of disease-modifying anti-Alzheimer drugs?

The preclinical phase of Alzheimer's disease represents a crucial time window for therapeutic intervention but requires the identification of clinically relevant biomarkers that are sensitive to the effects of disease-modifying drugs. Amyloid peptide and tau proteins, the main histological hallmarks of Alzheimer's disease, have been widely used as biomarkers of anti-amyloid and anti-tau drugs. However, these biomarkers do not fully capture the multiple biological pathways of the brain. Indeed, robust amyloid-target engagement by anti-amyloid monoclonal antibodies has recently translated into modest cognitive and clinical benefits in Alzheimer's disease patients, albeit with potentially life-threatening side effects. Moreover, targeting the tau pathway has yet to result in any positive clinical outcomes. Findings from computational neuroscience have demonstrated that brain regions do not work in isolation but are interconnected within complex network structures. Brain connectivity studies suggest that misfolded proteins can spread through these connections, leading to the hypothesis that Alzheimer's disease is a pathology of network disconnectivity. Based on these assumptions, here we discuss how incorporating brain connectivity outcomes could better capture global brain functionality and, in conjunction with traditional Alzheimer's disease biomarkers, could facilitate the clinical development of new disease-modifying anti-Alzheimer's disease drugs.

Cerebral hyperactivation across the Alzheimer's disease pathological cascade

Neuronal dysfunction in specific brain regions or across distributed brain networks is a known feature of Alzheimer's disease. An often reported finding in the early stage of the disease is the presence of increased functional MRI (fMRI) blood oxygenation level-dependent signal under task conditions relative to cognitively normal controls, a phenomenon known as 'hyperactivation'. However, research in the past decades yielded complex, sometimes conflicting results. The magnitude and topology of fMRI hyperactivation patterns have been found to vary across the preclinical and clinical spectrum of Alzheimer's disease, including concomitant 'hypoactivation' in some cases. These incongruences are likely due to a range of factors, including the disease stage at which the cohort is examined, the brain areas or networks studied and the fMRI paradigm utilized to evoke these functional abnormalities. Additionally, a perennial question pertains to the nature of hyperactivation in the context of Alzheimer's disease. Some propose it reflects compensatory mechanisms to sustain cognitive performance, while others suggest it is linked to the pathological disruption of a highly regulated homeostatic cycle that contributes to, or even drives, disease progression. Providing a coherent narrative for these empirical and conceptual discrepancies is paramount to develop disease models, understand the synergy between hyperactivation and the Alzheimer's disease pathological cascade and tailor effective interventions. We first provide a comprehensive overview of functional brain changes spanning the course from normal ageing to the clinical spectrum of Alzheimer's disease. We then highlight evidence supporting a close relationship between fMRI hyperactivation and in vivo markers of Alzheimer's pathology. We primarily focus on task-based fMRI studies in humans, but also consider studies using different functional imaging techniques and animal models. We then discuss the potential mechanisms underlying hyperactivation in the context of Alzheimer's disease and provide a testable framework bridging hyperactivation, ageing, cognition and the Alzheimer's disease pathological cascade. We conclude with a discussion of future challenges and opportunities to advance our understanding of the fundamental disease mechanisms of Alzheimer's disease, and the promising development of therapeutic interventions incorporating or aimed at hyperactivation and large-scale functional systems.

Small Molecules, α-Synuclein Pathology, and the Search for Effective Treatments in Parkinson's Disease

Parkinson's disease (PD) is a progressive age-related neurodegenerative disorder affecting millions of people worldwide. Essentially, it is characterised by selective degeneration of dopamine neurons of the nigro-striatal pathway and intraneuronal aggregation of misfolded α-synuclein with formation of Lewy bodies and Lewy neurites. Moreover, specific small molecules of intermediary metabolism may have a definite pathophysiological role in PD. These include dopamine, levodopa, reduced glutathione, glutathione disulfide/oxidised glutathione, and the micronutrients thiamine and ß-Hydroxybutyrate. Recent research indicates that these small molecules can interact with α-synuclein and regulate its folding and potential aggregation. In this review, we discuss the current knowledge on interactions between α-synuclein and both the small molecules of intermediary metabolism in the brain relevant to PD, and many other natural and synthetic small molecules that regulate α-synuclein aggregation. Additionally, we analyse some of the relevant molecular mechanisms potentially involved. A better understanding of these interactions may have relevance for the development of rational future therapies. In particular, our observations suggest that the micronutrients ß-Hydroxybutyrate and thiamine might have a synergistic therapeutic role in halting or reversing the progression of PD and other neuronal α-synuclein disorders.

Increased between-network connectivity: A risk factor for tau elevation and disease progression

One of the pathologic hallmarks of Alzheimer's disease (AD) is neurofibrillary tau tangles. Despite our knowledge that tau typically initiates in the medial temporal lobe (MTL), the mechanisms driving tau to spread beyond MTL remain unclear. Emerging evidence reveals distinct patterns of functional connectivity change during aging and preclinical AD: while connectivity within-network decreases, connectivity between-network increases. Building upon increased between-network connectivity, our study hypothesizes that this increase may play a critical role in facilitating tau spread in early stages. We conducted a longitudinal study over two to three years intervals on a cohort of 46 healthy elderly participants (mean age 64.23 ± 3.15 years, 26 females). Subjects were examined clinically and utilizing advanced imaging techniques that included resting-state functional MRI (rs-fMRI), structural magnetic resonance imaging (MRI), and a second-generation positron emission tomography (PET) tau tracer, 18F-MK6240. Through unsupervised agglomerative clustering and increase in between-network connectivity, we successfully identified individuals at increased risk of future tau elevation and AD progression. Our analysis revealed that individuals with increased between-network connectivity are more likely to experience more future tau deposition, entorhinal cortex thinning, and lower selective reminding test (SRT) delayed scores. Additionally, in the limbic network, we found a strong association between tau progression and increased between-network connectivity, which was mainly driven by beta-amyloid (Aβ) positive participants. These findings provide evidence for the hypothesis that an increase in between-network connectivity predicts future tau deposition and AD progression, also enhancing our understanding of AD pathogenesis in the preclinical stages.

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.

Outcomes and Mechanisms Associated With Selective Thalamic Neuronal Loss in Chronic Traumatic Brain Injury

Importance

The chronic neuronal burden of traumatic brain injury (TBI) is not fully characterized by routine imaging, limiting understanding of the role of neuronal substrates in adverse outcomes.

Objective

To determine whether tissues that appear healthy on routine imaging can be investigated for selective neuronal loss using [11C]flumazenil (FMZ) positron emission tomography (PET) and to examine whether this neuronal loss is associated with long-term outcomes.

Design, Setting, and Participants

In this cross-sectional study, data were collected prospectively from 2 centers (University of Cambridge in the UK and Weill Cornell Medicine in the US) between September 1, 2004, and May 31, 2021. Patients with TBI (>6 months postinjury) were compared with healthy control participants (all aged >18 years). Individuals with neurological disease, benzodiazepine use, or contraindication to magnetic resonance imaging were excluded. Data were retrospectively collated with nonconsecutive recruitment, owing to convenience and scanner or PET ligand availability. Data were analyzed between February 1 and September 30, 2023.

Exposure

Flumazenil voxelwise binding potential relative to nondisplaceable binding potential (BPND).

Main Outcomes and Measures

Selective neuronal loss identified with FMZ PET was compared between groups on voxelwise and regional scales, and its association with functional, cognitive, and psychological outcomes was examined using Glasgow Outcome Scale (GOS) scores, measures of sustained executive attention (animal and sustained fluency), and 36-Item Short Form Health Survey (SF-36) scores. Diffusion tensor imaging was used to assess structural connectivity of regions of cortical damage, and its association with thalamic selective neuronal loss.

Results

In this study, 24 patients with chronic TBI (mean [SD] age, 39.2 [12.3] years; 18 men [75.0%]) and 33 healthy control participants (mean [SD] age, 47.6 [20.5] years; 23 men [69.7%]) underwent FMZ PET. Patients with TBI had a median time of 29 (range, 7-95) months from injury to scan. They displayed selective neuronal loss in thalamic nuclei, over and above gross volume loss in the left thalamus, and bilateral central, mediodorsal, ventral-lateral dorsal, anterior, and ventral anterior thalamic nuclei, across a wide range of injury severities. Neuronal loss was associated with worse functional outcome using GOS scores (left thalamus, left ventral anterior, and bilateral central, mediodorsal, and anterior nuclei), worse cognitive outcome on measures of sustained executive attention (left thalamus, bilateral central, and right mediodorsal nuclei), and worse emotional outcome using SF-36 scores (right central thalamic nucleus). Chronic thalamic neuronal loss partially mirrored the location of primary cortical contusions, which may indicate secondary injury mechanisms of transneuronal degeneration.

Conclusions and Relevance

The findings of this study suggest that selective thalamic vulnerability may have chronic neuronal consequences with relevance to long-term outcome, suggesting the evolving and potentially lifelong thalamic neuronal consequences of TBI. FMZ PET is a more sensitive marker of the burden of neuronal injury than routine imaging; therefore, it could inform outcome prognostication and may lead to the development of individualized precision medicine approaches.

Advances in semantic dementia: Neuropsychology, pathology & neuroimaging

Semantic dementia is a kind of neurodegenerative disorder, characterized by prominent semantic impairments and anterior temporal lobe atrophy. Since 2010, more studies have devoted to this rare disorder, revealing that it is more complex than we think. Clinical advances include more specific findings of semantic impairments and other higher order cognitive deficits. Neuroimaging techniques can help revealing the different brain networks affected (both structurally and functionally) in this condition. Pathological and genetic studies have also found more complex situations of semantic dementia, which might explain the huge variance existing in semantic dementia. Moreover, the current diagnosis criteria mainly focus on semantic dementia's classical prototype. We further delineated the features of three subtypes of semantic dementia based on atrophy lateralization with three severity stages. In a broader background, as a part of the continuum of neurodegenerative disorders, semantic dementia is commonly compared with other resembling conditions. Therefore, we summarized the differential diagnosis between semantic dementia and them. Finally, we introduced the challenges and achievements of its diagnosis, treatment, care and cross cultural comparison. By providing a comprehensive picture of semantic dementia on different aspects of advances, we hope to deepen the understanding of semantic dementia and promote more inspirations on both clinical and theoretical studies about it.

Can neurodevelopmental disorders influence the course of neurodegenerative diseases? A scoping review

This scoping review aims at giving an overview of the possible influence of neurodevelopmental disorders (NDDs) on cognitive-behavioral neurodegenerative diseases (CBNDs). Based on the PRISMA-ScR checklist, it details the methods of NDDs screening, the identified NDDs-CBNDs associations, as well as the criteria and types of association. The last literature search was performed in June 2023. In the final study, 32 articles were included. Analysis first showed that NDDs were mainly detected through medical records screening. Second, the association of specific learning disorders and major or mild neurocognitive disorder due to Alzheimer's disease was the most investigated. Third, associations were mostly based on prevalence comparisons. Finally, 66 % of studies reported a positive association between NDDs and CBNDs. Notably, up to 67 % of positive associations were observed with atypical forms of certain CBNDs. Authors' interpretations suggest that NDDs could constitute a risk factor for CBNDs. However, the influence of NDDs on CBNDs still lacks evidence and biological support, possibly due to the heterogeneity of methods and criteria employed. Developing validated assessment tools for all NDDs and conducting cohort studies could be beneficial for research, and clinical practice. Indeed, this review also underlines the importance of adopting a life-span approach regarding CBNDs.

Neuroanatomical and cognitive biomarkers of alpha-synuclein propagation in a mouse model of synucleinopathy prior to onset of motor symptoms

Significant evidence suggests that misfolded alpha-synuclein (aSyn), a major component of Lewy bodies, propagates in a prion-like manner contributing to disease progression in Parkinson's disease (PD) and other synucleinopathies. In fact, timed inoculation of M83 hemizygous mice with recombinant human aSyn preformed fibrils (PFF) has shown symptomatic deficits after substantial spreading of pathogenic alpha-synuclein, as detected by markers for the phosphorylation of S129 of aSyn. However, whether accumulated toxicity impact human-relevant cognitive and structural neuroanatomical measures is not fully understood. Here we performed a single unilateral striatal PFF injection in M83 hemizygous mice, and using two assays with translational potential, ex vivo magnetic resonance imaging (MRI) and touchscreen testing, we examined the combined neuroanatomical and behavioral impact of aSyn propagation. In PFF-injected mice, we observed widespread atrophy in bilateral regions that project to or receive input from the injection site using MRI. We also identified early deficits in reversal learning prior to the emergence of motor symptoms. Our findings highlight a network of regions with related cellular correlates of pathology that follow the progression of aSyn spreading, and that affect brain areas relevant for reversal learning. Our experiments suggest that M83 hemizygous mice injected with human PFF provides a model to understand how misfolded aSyn affects human-relevant pre-clinical measures and suggest that these pre-clinical biomarkers could be used to detect early toxicity of aSyn and provide better translational measures between mice and human disease.

Directionality bias underpins divergent spatiotemporal progression of Alzheimer-related tauopathy in mouse models

Mounting evidence implicates trans-synaptic connectome-based spread as a shared mechanism behind different tauopathic conditions, yet also suggests there is divergent spatiotemporal progression between them. A potential parsimonious explanation for this apparent contradiction could be that different conditions incur differential rates and directional biases in tau transmission along fiber tracts. In this meta-analysis we closely examined this hypothesis and quantitatively tested it using spatiotemporal tau pathology patterns from 11 distinct models across 4 experimental studies. For this purpose, we extended a network-based spread model by incorporating net directionality along the connectome. Our data unambiguously supports the directional transmission hypothesis. First, retrograde bias is an unambiguously better predictor of tau progression than anterograde bias. Second, while spread exhibits retrograde character, our best-fitting biophysical models incorporate the mixed effects of both retrograde- and anterograde-directed spread, with notable tau-strain-specific differences. We also found a nontrivial association between directionality bias and tau strain aggressiveness, with more virulent strains exhibiting less retrograde character. Taken together, our study implicates directional transmission bias in tau transmission along fiber tracts as a general feature of tauopathy spread and a strong candidate explanation for the diversity of spatiotemporal tau progression between conditions. This simple and parsimonious mechanism may potentially fill a critical gap in our knowledge of the spatiotemporal ramification of divergent tauopathies.

Altered spreading of fast aperiodic brain waves relates to disease duration in Amyotrophic Lateral Sclerosis

OBJECTIVE

To test the hypothesis that patients affected by Amyotrophic Lateral Sclerosis (ALS) show an altered spatio-temporal spreading of neuronal avalanches in the brain, and that this may related to the clinical picture.

METHODS

We obtained the source-reconstructed magnetoencephalography (MEG) signals from thirty-six ALS patients and forty-two healthy controls. Then, we used the construct of the avalanche transition matrix (ATM) and the corresponding network parameter nodal strength to quantify the changes in each region, since this parameter provides key information about which brain regions are mostly involved in the spreading avalanches.

RESULTS

ALS patients presented higher values of the nodal strength in both cortical and sub-cortical brain areas. This parameter correlated directly with disease duration.

CONCLUSIONS

In this work, we provide a deeper characterization of neuronal avalanches propagation in ALS, describing their spatio-temporal trajectories and identifying the brain regions most likely to be involved in the process. This makes it possible to recognize the brain areas that take part in the pathogenic mechanisms of ALS. Furthermore, the nodal strength of the involved regions correlates directly with disease duration.

SIGNIFICANCE

Our results corroborate the clinical relevance of aperiodic, fast large-scale brain activity as a biomarker of microscopic changes induced by neurophysiological processes.

Gray matter atrophy is constrained by normal structural brain network architecture in depression

BACKGROUND

There is growing evidence that gray matter atrophy is constrained by normal brain network (or connectome) architecture in neuropsychiatric disorders. However, whether this finding holds true in individuals with depression remains unknown. In this study, we aimed to investigate the association between gray matter atrophy and normal connectome architecture at individual level in depression.

METHODS

In this study, 297 patients with depression and 256 healthy controls (HCs) from two independent Chinese dataset were included: a discovery dataset (105 never-treated first-episode patients and matched 130 HCs) and a replication dataset (106 patients and matched 126 HCs). For each patient, individualized regional atrophy was assessed using normative model and brain regions whose structural connectome profiles in HCs most resembled the atrophy patterns were identified as putative epicenters using a backfoward stepwise regression analysis.

RESULTS

In general, the structural connectome architecture of the identified disease epicenters significantly explained 44% (±16%) variance of gray matter atrophy. While patients with depression demonstrated tremendous interindividual variations in the number and distribution of disease epicenters, several disease epicenters with higher participation coefficient than randomly selected regions, including the hippocampus, thalamus, and medial frontal gyrus were significantly shared by depression. Other brain regions with strong structural connections to the disease epicenters exhibited greater vulnerability. In addition, the association between connectome and gray matter atrophy uncovered two distinct subgroups with different ages of onset.

CONCLUSIONS

These results suggest that gray matter atrophy is constrained by structural brain connectome and elucidate the possible pathological progression in depression.

Predictive spread of obsessive-compulsive disorder pathology using the network diffusion model

An increasing body of studies propose that structural abnormalities begin with focal brain regions then propagate to other regions following the architecture of healthy brain network in neuropsychiatric disorders. However, these findings are untested in obsessive-compulsive disorder (OCD), also showing widespread structural brain abnormalities. In this study, we aimed to investigate whether healthy functional brain network contributed to structural brain abnormalities in OCD. The gray matter morphological abnormalities were obtained in 98 patients with OCD in relative to matched healthy controls (n = 130, HCs). The network diffusion model (NDM) was conducted to identify putative seed regions and patterns of disease propagation from seed regions to other brain regions along the functional network in OCD. The NDM has been proved to succeeded in capturing the trans-neuronal propagation of pathology and even in predicting future longitudinal progression of pathology in neurodegenerative diseases. In this study, when seeding at the right anterior cingulate cortex, the NDM best recapitulated the patterns of gray matter morphological abnormalities, suggesting this region was the most likely seed region. Further analyses revealed that pathology preferentially propagated to higher order brain systems from seed region. For non-seed regions, the arrival time of pathology was negatively correlated with their shortest functional paths to the seed (r = -0.46, p < 0.001). These results suggest that gray matter morphological abnormalities are constrained by healthy brain network and reveal temporal sequencing of pathology progression in OCD.

Connectome architecture modulates the gray matter atrophy in major depression disorder patients with diverse suicidal ideations

Gray matter (GM) atrophy is well documented in patients with major depressive disorder (MDD), but its underlying mechanism remains unknown. This study aimed to examine the GM atrophy in MDD patients with diverse suicidal ideations (SIs) and to explore whether those alterations were driven by connections. GM volume was estimated in 163 patients with recurrent MDD (comprising 122 with SI [MDDSI] and 41 without SI [MDDNSI]) and 134 health controls (HCs). A two-sample t-test was used to identify GM volume abnormalities in MDD patients and their subgroups. Functional connectivity was computed between pairs of aberrant GM in both patients and HCs, which were further compared with the connectivity of random brain regions. A permutation test was performed to assess its significance. Propensity score matching (PSM) was further performed to validate the main results. Compared with HCs, the MDDNSI group exhibited GM atrophy in 24 regions, with the largest effect sizes found in the frontal and parietal lobes, while the MDDSI group exhibited more widespread GM atrophy involving 49 regions, with the largest effect sizes in the frontal lobe, parietal lobe, temporal lobe, and the limbic system. Furthermore, patients and HCs exhibited significantly increased functional connectivity between regions with GM atrophy compared with randomly selected regions (p < 0.05). PSM analysis presented similar results to the main analysis. MDD patients had diverse GM atrophy features according to their SI tendency. Moreover, connectome architecture modulates the GM atrophy in MDD patients, implying the possibility that connections drive these pathological changes.

Selective vulnerability and resilience to Alzheimer's disease tauopathy as a function of genes and the connectome

Brain regions in Alzheimer's (AD) exhibit distinct vulnerability to the disease's hallmark pathology, with the entorhinal cortex and hippocampus succumbing early to tau tangles while others like primary sensory cortices remain resilient. The quest to understand how local/regional genetic factors, pathogenesis, and network-mediated spread of pathology together govern this selective vulnerability (SV) or resilience (SR) is ongoing. Although many risk genes in AD are known from gene association and transgenic studies, it is still not known whether and how their baseline expression signatures confer SV or SR to brain structures. Prior analyses have yielded conflicting results, pointing to a disconnect between the location of genetic risk factors and downstream tau pathology. We hypothesize that a full accounting of genes' role in mediating SV/SR would require the modeling of network-based vulnerability, whereby tau misfolds, aggregates, and propagates along fiber projections. We therefore employed an extended network diffusion model (eNDM) and tested it on tau pathology PET data from 196 AD patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Thus the fitted eNDM model becomes a reference process from which to assess the role of innate genetic factors. Using the residual (observed - model-predicted) tau as a novel target outcome, we obtained its association with 100 top AD risk-genes, whose baseline spatial transcriptional profiles were obtained from the Allen Human Brain Atlas (AHBA). We found that while many risk genes at baseline showed a strong association with regional tau, many more showed a stronger association with residual tau. This suggests that both direct vulnerability, related to the network, as well as network-independent vulnerability, are conferred by risk genes. We then classified risk genes into four classes: network-related SV (SV-NR), network-independent SV (SV-NI), network-related SR (SR-NR), and network-independent SR (SR-NI). Each class has a distinct spatial signature and associated vulnerability to tau. Remarkably, we found from gene-ontology analyses, that genes in these classes were enriched in distinct functional processes and encompassed different functional networks. These findings offer new insights into the factors governing innate vulnerability or resilience in AD pathophysiology and may prove helpful in identifying potential intervention targets.

Gray matter morphological abnormities are constrained by normal structural covariance network in OCD

A growing body of evidences reveal that abnormal gray matter morphology is constrained by normal brain network architecture in neurodegenerative and psychiatric disorders. However, whether this finding holds true in obsessive-compulsive disorder (OCD) remains unknown. In the current study, we aimed to investigate the association between gray matter morphological abnormities and normal structural covariance network architecture in OCD. First, gray matter morphological abnormities were obtained between 98 medicine-naive and first-episode patients with OCD and 130 healthy controls (HCs). Then, putative disease epicenters whose structural connectome profiles in HCs most resembled the morphological differences pattern were identified using a backfoward stepwise regression analysis. A set of brain regions were identified as putative disease epicenters whose structural connectome architecture significantly explained 59.94% variance of morphological abnormalities. These disease epicenters comprised brain regions implicated in high-order cognitive functions and sensory/motor processing. Other brain regions with stronger structural connections to disease epicenters exhibited greater vulnerability to disease. Together, these results suggest that gray matter abnormities are constrained by structural connectome and provide new insights into the possible pathological progression in OCD.

Primary progressive aphasia: six questions in search of an answer

Here, we review recent progress in the diagnosis and management of primary progressive aphasia-the language-led dementias. We pose six key unanswered questions that challenge current assumptions and highlight the unresolved difficulties that surround these diseases. How many syndromes of primary progressive aphasia are there-and is syndromic diagnosis even useful? Are these truly 'language-led' dementias? How can we diagnose (and track) primary progressive aphasia better? Can brain pathology be predicted in these diseases? What is their core pathophysiology? In addition, how can primary progressive aphasia best be treated? We propose that pathophysiological mechanisms linking proteinopathies to phenotypes may help resolve the clinical complexity of primary progressive aphasia, and may suggest novel diagnostic tools and markers and guide the deployment of effective therapies.

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.

Altered local and remote functional connectivity in mild Alzheimer's disease patients with sleep disturbances

Objectives

This study aimed to investigate local and remote functional connectivity in mild Alzheimer's disease patients with sleep disturbances (ADSD) and those without sleep disturbances (ADNSD).

Methods

Thirty eight mild AD patients with sleep disturbances and 21 mild AD patients without sleep disturbances participated in this study. All subjects underwent neuropsychological assessments and 3.0 Tesla magnetic resonance scanning. Static and dynamic regional homogeneity (ReHo) were used to represent the local functional connectivity. Seed-based whole-brain functional connectivity was used to represent the remote functional connectivity. The seed was chosen based on the results of ReHo.

Results

Compared to ADNSD, ADSD showed decreased static ReHo in the left posterior central gyrus and the right cuneus and increased dynamic ReHo in the left posterior central gyrus. As for the remote functional connectivity, comparing ADSD to ADNSD, it was found that there was a decreased functional connection between the left posterior central gyrus and the left cuneus as well as the left calcarine.

Conclusion

The current study demonstrated that, compared with ADNSD, ADSD is impaired in both local and remote functional connectivity, manifested as reduced functional connectivity involving the primary sensory network and the primary visual network. The abnormality of the above functional connectivity is one of the reasons why sleep disorders promote cognitive impairment in AD. Moreover, sleep disorders change the temporal sequence of AD pathological damage to brain functional networks, but more evidence is needed to support this conclusion.

Network connectivity and local transcriptomic vulnerability underpin cortical atrophy progression in Parkinson's disease

Parkinson's disease pathology is hypothesized to spread through the brain via axonal connections between regions and is further modulated by local vulnerabilities within those regions. The resulting changes to brain morphology have previously been demonstrated in both prodromal and de novo Parkinson's disease patients. However, it remains unclear whether the pattern of atrophy progression in Parkinson's disease over time is similarly explained by network-based spreading and local vulnerability. We address this gap by mapping the trajectory of cortical atrophy rates in a large, multi-centre cohort of Parkinson's disease patients and relate this atrophy progression pattern to network architecture and gene expression profiles. Across 4-year follow-up visits, increased atrophy rates were observed in posterior, temporal, and superior frontal cortices. We demonstrated that this progression pattern was shaped by network connectivity. Regional atrophy rates were strongly related to atrophy rates across structurally and functionally connected regions. We also found that atrophy progression was associated with specific gene expression profiles. The genes whose spatial distribution in the brain was most related to atrophy rate were those enriched for mitochondrial and metabolic function. Taken together, our findings demonstrate that both global and local brain features influence vulnerability to neurodegeneration in Parkinson's disease.

Integrating multimodal and multiscale connectivity blueprints of the human cerebral cortex in health and disease

The brain is composed of disparate neural populations that communicate and interact with one another. Although fiber bundles, similarities in molecular architecture, and synchronized neural activity all reflect how brain regions potentially interact with one another, a comprehensive study of how all these interregional relationships jointly reflect brain structure and function remains missing. Here, we systematically integrate 7 multimodal, multiscale types of interregional similarity ("connectivity modes") derived from gene expression, neurotransmitter receptor density, cellular morphology, glucose metabolism, haemodynamic activity, and electrophysiology in humans. We first show that for all connectivity modes, feature similarity decreases with distance and increases when regions are structurally connected. Next, we show that connectivity modes exhibit unique and diverse connection patterns, hub profiles, spatial gradients, and modular organization. Throughout, we observe a consistent primacy of molecular connectivity modes-namely correlated gene expression and receptor similarity-that map onto multiple phenomena, including the rich club and patterns of abnormal cortical thickness across 13 neurological, psychiatric, and neurodevelopmental disorders. Finally, to construct a single multimodal wiring map of the human cortex, we fuse all 7 connectivity modes and show that the fused network maps onto major organizational features of the cortex including structural connectivity, intrinsic functional networks, and cytoarchitectonic classes. Altogether, this work contributes to the integrative study of interregional relationships in the human cerebral cortex.

Network models to enhance the translational impact of cross-species studies

Neuroscience studies are often carried out in animal models for the purpose of understanding specific aspects of the human condition. However, the translation of findings across species remains a substantial challenge. Network science approaches can enhance the translational impact of cross-species studies by providing a means of mapping small-scale cellular processes identified in animal model studies to larger-scale inter-regional circuits observed in humans. In this Review, we highlight the contributions of network science approaches to the development of cross-species translational research in neuroscience. We lay the foundation for our discussion by exploring the objectives of cross-species translational models. We then discuss how the development of new tools that enable the acquisition of whole-brain data in animal models with cellular resolution provides unprecedented opportunity for cross-species applications of network science approaches for understanding large-scale brain networks. We describe how these tools may support the translation of findings across species and imaging modalities and highlight future opportunities. Our overarching goal is to illustrate how the application of network science tools across human and animal model studies could deepen insight into the neurobiology that underlies phenomena observed with non-invasive neuroimaging methods and could simultaneously further our ability to translate findings across species.

The noradrenergic subtype of Parkinson disease: from animal models to clinical practice

Many advances in understanding the pathophysiology of Parkinson disease (PD) have been based on research addressing its motor symptoms and phenotypes. Various data-driven clinical phenotyping studies supported by neuropathological and in vivo neuroimaging data suggest the existence of distinct non-motor endophenotypes of PD even at diagnosis, a concept further strengthened by the predominantly non-motor spectrum of symptoms in prodromal PD. Preclinical and clinical studies support early dysfunction of noradrenergic transmission in both the CNS and peripheral nervous system circuits in patients with PD that results in a specific cluster of non-motor symptoms, including rapid eye movement sleep behaviour disorder, pain, anxiety and dysautonomia (particularly orthostatic hypotension and urinary dysfunction). Cluster analyses of large independent cohorts of patients with PD and phenotype-focused studies have confirmed the existence of a noradrenergic subtype of PD, which had been previously postulated but not fully characterized. This Review discusses the translational work that unravelled the clinical and neuropathological processes underpinning the noradrenergic PD subtype. Although some overlap with other PD subtypes is inevitable as the disease progresses, recognition of noradrenergic PD as a distinct early disease subtype represents an important advance towards the delivery of personalized medicine for patients with PD.

The pollutome-connectome axis: a putative mechanism to explain pollution effects on neurodegeneration

The study of pollutant effects is extremely important to address the epochal challenges we are facing, where world populations are increasingly moving from rural to urban centers, revolutionizing our world into an urban world. These transformations will exacerbate pollution, thus highlighting the necessity to unravel its effect on human health. Epidemiological studies have reported that pollution increases the risk of neurological diseases, with growing evidence on the risk of neurodegenerative disorders. Air pollution and water pollutants are the main chemicals driving this risk. These chemicals can promote inflammation, acting in synergy with genotype vulnerability. However, the biological underpinnings of this association are unknown. In this review, we focus on the link between pollution and brain network connectivity at the macro-scale level. We provide an updated overview of epidemiological findings and studies investigating brain network changes associated with pollution exposure, and discuss the mechanistic insights of pollution-induced brain changes through neural networks. We explain, in detail, the pollutome-connectome axis that might provide the functional substrate for pollution-induced processes leading to cognitive impairment and neurodegeneration. We describe this model within the framework of two pollutants, air pollution, a widely recognized threat, and polyfluoroalkyl substances, a large class of synthetic chemicals which are currently emerging as new neurotoxic source.

Connectome-based biophysics models of Alzheimer's disease diagnosis and prognosis

With the increasing prevalence of Alzheimer's disease (AD) among aging populations and the limited therapeutic options available to slow or reverse its progression, the need has never been greater for improved diagnostic tools for identifying patients in the preclinical and prodomal phases of AD. Biophysics models of the connectome-based spread of amyloid-beta (Aβ) and microtubule-associated protein tau (τ) have enjoyed recent success as tools for predicting the time course of AD-related pathological changes. However, given the complex etiology of AD, which involves not only connectome-based spread of protein pathology but also the interactions of many molecular and cellular players over multiple spatiotemporal scales, more robust, complete biophysics models are needed to better understand AD pathophysiology and ultimately provide accurate patient-specific diagnoses and prognoses. Here we discuss several areas of active research in AD whose insights can be used to enhance the mathematical modeling of AD pathology as well as recent attempts at developing improved connectome-based biophysics models. These efforts toward a comprehensive yet parsimonious mathematical description of AD hold great promise for improving both the diagnosis of patients at risk for AD and our mechanistic understanding of how AD progresses.

Structural MRI predicts clinical progression in presymptomatic genetic frontotemporal dementia: findings from the GENetic Frontotemporal dementia Initiative cohort

Biomarkers that can predict disease progression in individuals with genetic frontotemporal dementia are urgently needed. We aimed to identify whether baseline MRI-based grey and white matter abnormalities are associated with different clinical progression profiles in presymptomatic mutation carriers in the GENetic Frontotemporal dementia Initiative. Three hundred eighty-seven mutation carriers were included (160 GRN, 160 C9orf72, 67 MAPT), together with 240 non-carrier cognitively normal controls. Cortical and subcortical grey matter volumes were generated using automated parcellation methods on volumetric 3T T1-weighted MRI scans, while white matter characteristics were estimated using diffusion tensor imaging. Mutation carriers were divided into two disease stages based on their global CDR®+NACC-FTLD score: presymptomatic (0 or 0.5) and fully symptomatic (1 or greater). The w-scores in each grey matter volumes and white matter diffusion measures were computed to quantify the degree of abnormality compared to controls for each presymptomatic carrier, adjusting for their age, sex, total intracranial volume, and scanner type. Presymptomatic carriers were classified as 'normal' or 'abnormal' based on whether their grey matter volume and white matter diffusion measure w-scores were above or below the cut point corresponding to the 10th percentile of the controls. We then compared the change in disease severity between baseline and one year later in both the 'normal' and 'abnormal' groups within each genetic subtype, as measured by the CDR®+NACC-FTLD sum-of-boxes score and revised Cambridge Behavioural Inventory total score. Overall, presymptomatic carriers with normal regional w-scores at baseline did not progress clinically as much as those with abnormal regional w-scores. Having abnormal grey or white matter measures at baseline was associated with a statistically significant increase in the CDR®+NACC-FTLD of up to 4 points in C9orf72 expansion carriers, and 5 points in the GRN group as well as a statistically significant increase in the revised Cambridge Behavioural Inventory of up to 11 points in MAPT, 10 points in GRN, and 8 points in C9orf72 mutation carriers. Baseline regional brain abnormalities on MRI in presymptomatic mutation carriers are associated with different profiles of clinical progression over time. These results may be helpful to inform stratification of participants in future trials.

Default mode network failure and neurodegeneration across aging and amnestic and dysexecutive Alzheimer's disease

From a complex systems perspective, clinical syndromes emerging from neurodegenerative diseases are thought to result from multiscale interactions between aggregates of misfolded proteins and the disequilibrium of large-scale networks coordinating functional operations underpinning cognitive phenomena. Across all syndromic presentations of Alzheimer's disease, age-related disruption of the default mode network is accelerated by amyloid deposition. Conversely, syndromic variability may reflect selective neurodegeneration of modular networks supporting specific cognitive abilities. In this study, we leveraged the breadth of the Human Connectome Project-Aging cohort of non-demented individuals (N = 724) as a normative cohort to assess the robustness of a biomarker of default mode network dysfunction in Alzheimer's disease, the network failure quotient, across the aging spectrum. We then examined the capacity of the network failure quotient and focal markers of neurodegeneration to discriminate patients with amnestic (N = 8) or dysexecutive (N = 10) Alzheimer's disease from the normative cohort at the patient level, as well as between Alzheimer's disease phenotypes. Importantly, all participants and patients were scanned using the Human Connectome Project-Aging protocol, allowing for the acquisition of high-resolution structural imaging and longer resting-state connectivity acquisition time. Using a regression framework, we found that the network failure quotient related to age, global and focal cortical thickness, hippocampal volume, and cognition in the normative Human Connectome Project-Aging cohort, replicating previous results from the Mayo Clinic Study of Aging that used a different scanning protocol. Then, we used quantile curves and group-wise comparisons to show that the network failure quotient commonly distinguished both dysexecutive and amnestic Alzheimer's disease patients from the normative cohort. In contrast, focal neurodegeneration markers were more phenotype-specific, where the neurodegeneration of parieto-frontal areas associated with dysexecutive Alzheimer's disease, while the neurodegeneration of hippocampal and temporal areas associated with amnestic Alzheimer's disease. Capitalizing on a large normative cohort and optimized imaging acquisition protocols, we highlight a biomarker of default mode network failure reflecting shared system-level pathophysiological mechanisms across aging and dysexecutive and amnestic Alzheimer's disease and biomarkers of focal neurodegeneration reflecting distinct pathognomonic processes across the amnestic and dysexecutive Alzheimer's disease phenotypes. These findings provide evidence that variability in inter-individual cognitive impairment in Alzheimer's disease may relate to both modular network degeneration and default mode network disruption. These results provide important information to advance complex systems approaches to cognitive aging and degeneration, expand the armamentarium of biomarkers available to aid diagnosis, monitor progression and inform clinical trials.

Sex differences in grey matter networks in dementia with Lewy bodies

Objectives

Sex differences permeate many aspects of dementia with Lewy bodies (DLB), including epidemiology, pathogenesis, disease progression, and symptom manifestation. However, less is known about potential sex differences in patterns of neurodegeneration in DLB. Here, we test whether grey matter networks also differ between female and male DLB patients. To assess the specificity of these sex differences to DLB, we additionally investigate sex differences in healthy controls (HCs).

Methods

A total of 119 (68.7 ± 8.4 years) male and 45 female (69.9 ± 9.1 years) DLB patients from three European centres and the Mayo Clinic were included in this study. Additionally, we included 119 male and 45 female age-matched HCs from the Mayo Clinic. Grey matter volumes of 58 cortical, subcortical, cerebellar, and pontine brain regions derived from structural magnetic resonance images were corrected for age, intracranial volume, and centre. Sex-specific grey matter networks for DLB patients and HCs were constructed by correlating each pair of brain regions. Network properties of the correlation matrices were compared between sexes and groups. Additional analyses were conducted on W-scored data to identify DLB-specific findings.

Results

Networks of male HCs and male DLB patients were characterised by a lower nodal strength compared to their respective female counterparts. In comparison to female HCs, the grey matter networks of male HCs showed a higher global efficiency, modularity, and a lower number of modules. None of the global and nodal network measures showed significant sex differences in DLB.

Conclusions

The disappearance of sex differences in the structural grey matter networks of DLB patients compared to HCs may indicate a sex-dependent network vulnerability to the alpha-synuclein pathology in DLB. Future studies might investigate whether the differences in structural network measures are associated with differences in cognitive scores and clinical symptoms between the sexes.

Brain Metabolic Profile in Presymptomatic GRN Carriers Throughout a 5-Year Follow-up

BACKGROUND AND OBJECTIVES

GRN variants are a frequent cause of familial frontotemporal dementia (FTD). Monitoring disease progression in asymptomatic carriers of genetic variants is a major challenge in delivering preventative therapies before clinical onset. This study aimed to assess the usefulness of fluorodeoxyglucose (FDG)-PET in identifying metabolic changes in presymptomatic GRN carriers (PS-GRN+) and to trace their longitudinal progression.

METHODS

Participants were longitudinally evaluated over 5 years in a prospective cohort study focused on GRN disease (Predict-PGRN). They underwent cognitive/behavioral assessment, plasma neurofilament measurement, brain MRI, and FDG-PET. Voxel-wise comparisons of structural and metabolic imaging data between 2 groups were performed for each time point. Longitudinal PET changes were evaluated with voxel-wise comparisons and the metabolic percent annual changes method. The association of regional brain metabolism with plasma neurofilament and cognitive changes was analyzed.

RESULTS

Among the 80 individuals enrolled in the study, 58 (27 PS-GRN+ and 31 noncarriers) were included in the analyses. Cross-sectional comparisons between PS-GRN+ and controls found a significant hypometabolism in the left superior temporal sulcus (STS) region (encompassing the middle and superior temporal gyri), approximately 15 years before the expected disease onset, without significant cortical atrophy. The longitudinal metabolic decline over the following 5 years peaked around the right STS in carriers (p < 0.001), without significantly greater volume loss compared with that in controls. Their estimated annualized metabolic decrease (-1.37%) was higher than that in controls (-0.21%, p = 0.004). Lower glucose uptake was associated with higher neurofilament increase (p = 0.003) and lower frontal cognitive scores (p = 0.014) in PS-GRN+.

DISCUSSION

This study detected brain metabolic changes in the STS region, preceding structural and cognitive alterations, thus contributing to the characterization of the pathochronology of preclinical GRN disease. Owing to the STS involvement in the perception of facially communicated cues, it is likely that its dysfunction contributes to social cognition deficits characterizing FTD. Overall, our study highlights brain metabolic changes as an early disease-tracking biomarker and proposes annualized percent decrease as a metric to monitor therapeutic response in forthcoming trials.

The effects of microglia on tauopathy progression can be quantified using Nexopathy in silico (Nexis) models

The prion-like transsynaptic propagation of misfolded tau along the brain's connectome has previously been modeled using connectome-based network diffusion models. In addition to the connectome, interactions between the general neurological "milieu" in the neurodegenerative brain and proteinopathic species can also contribute to pathology propagation. Such a molecular nexopathy framework posits that the distinct characteristics of neurodegenerative disorders stem from interactions between the network and surrounding molecular players. However, the effects of these modulators remain unquantified. Here, we present Nexopathy in silico ("Nexis"), a quantitative model of tau progression augmenting earlier models by including parameters of pathology propagation defined by the molecular modulators of connectome-based spread. Our Nexis:microglia model provides the first quantitative characterization of this effect on the whole brain by expanding previous models of neuropathology progression by incorporating microglial influence. We show that Trem2, but not microglial homeostasis genes, significantly improved the model's predictive power. Trem2 appears to reduce tau accumulation rate while increasing its interregional spread from the hippocampal seed area, causing higher tau burden in the striatum, pallidum, and contralateral hippocampus. Nexis provides an improved understanding and quantification of microglial contribution to tau propagation and can be flexibly modified to include other modulators of progressive neurodegeneration.

Co-alteration Network Architecture of Major Depressive Disorder: A Multi-modal Neuroimaging Assessment of Large-scale Disease Effects

Major depressive disorder (MDD) exhibits diverse symptomology and neuroimaging studies report widespread disruption of key brain areas. Numerous theories underpinning the network degeneration hypothesis (NDH) posit that neuropsychiatric diseases selectively target brain areas via meaningful network mechanisms rather than as indistinct disease effects. The present study tests the hypothesis that MDD is a network-based disorder, both structurally and functionally. Coordinate-based meta-analysis and Activation Likelihood Estimation (CBMA-ALE) were used to assess the convergence of findings from 92 previously published studies in depression. An extension of CBMA-ALE was then used to generate a node-and-edge network model representing the co-alteration of brain areas impacted by MDD. Standardized measures of graph theoretical network architecture were assessed. Co-alteration patterns among the meta-analytic MDD nodes were then tested in independent, clinical T1-weighted structural magnetic resonance imaging (MRI) and resting-state functional (rs-fMRI) data. Differences in co-alteration profiles between MDD patients and healthy controls, as well as between controls and clinical subgroups of MDD patients, were assessed. A 65-node 144-edge co-alteration network model was derived for MDD. Testing of co-alteration profiles in replication data using the MDD nodes provided distinction between MDD and healthy controls in structural data. However, co-alteration profiles were not distinguished between patients and controls in rs-fMRI data. Improved distinction between patients and healthy controls was observed in clinically homogenous MDD subgroups in T1 data. MDD abnormalities demonstrated both structural and functional network architecture, though only structural networks exhibited between-groups differences. Our findings suggest improved utility of structural co-alteration networks for ongoing biomarker development.

Finding Common Ground on the Site of Onset of Amyotrophic Lateral Sclerosis

The fundamental origin of amyotrophic lateral sclerosis (ALS) has remained an enigma since its earliest description as a relentlessly progressive degeneration with prominent neuromuscular manifestations that are associated with upper and lower motor neuron dysfunction. Although this remains the hallmark of ALS, a significant proportion of patients will also demonstrate one or more features of frontotemporal dysfunction, including a frontotemporal dementia (FTD). Understanding whether these 2 seemingly disparate syndromes are simply reflective of the co-occurrence of 2 distinct pathologic processes or the clinical manifestations of a common pathophysiologic derangement involving the brain more widely has gripped contemporary ALS researchers. Supporting a commonality of causation, both ALS and FTD show an alteration in the metabolism of TAR DNA-binding protein 43, marked by a shift in nucleocytoplasmic localization alongside a broad range of neuronal cytoplasmic inclusions consisting of pathologic aggregates of RNA-binding proteins. Similarly, several disease-associated or disease-modifying genetic variants that are shared between the 2 disorders suggest shared underlying mechanisms. In both, a prominent glial response has been postulated to contribute to non-cell-autonomous spread. A more contemporary hypothesis, however, suggests that syndromes of cortical and subcortical dysfunction are driven by impairments in discrete neural networks. This postulates that such networks, including networks subserving motor or cognitive function, possess unique and selective vulnerabilities to either single molecular toxicities or combinations thereof. The co-occurrence of one or more network dysfunctions in ALS and FTD is thus a reflection not of unique neuroanatomic correlates but rather of shared molecular vulnerabilities. The basis of such shared vulnerabilities becomes the fulcrum around which the next advances in our understanding of ALS and its possible therapy will develop.

Interplay of tau and functional network connectivity in progressive supranuclear palsy: a [18F]PI-2620 PET/MRI study

PURPOSE

Progressive supranuclear palsy (PSP) is primary 4-repeat tauopathy. Evidence spanning from imaging studies indicate aberrant connectivity in PSPs. Our goal was to assess functional connectivity network alterations in PSP patients and the potential link between regional tau-burden and network-level functional connectivity using the next-generation tau PET tracer [18F]PI-2620 and resting-state functional MRI (fMRI).

MATERIAL AND METHODS

Twenty-four probable PSP patients (70.9 ± 6.9 years, 13 female), including 14 Richardson syndrome (RS) and 10 non-RS phenotypes, underwent [18F]PI-2620 PET/MRI imaging. Distribution volume ratios (DVRs) were estimated using non-invasive pharmacokinetic modeling. Resting-state fMRI was also acquired in these patients as well as in thirteen older non-AD MCI reference group (64 ± 9 years, 4 female). The functional network was constructed using 141 by 141 region-to-region functional connectivity metrics (RRC) and network-based statistic was carried out (connection threshold p < 0.001, cluster threshold pFDR < 0.05).

RESULTS

In total, 9870 functional connections were analyzed. PSPs compared to aged non-AD MCI reference group expressed aberrant connectivity evidenced by the significant NBS network consisting of 89 ROIs and 118 connections among them (NBS mass 4226, pFDR < 0.05). Tau load in the right globus pallidus externus (GPe) and left dentate nucleus (DN) showed significant effects on functional network connectivity. The network linked with increased tau load in the right GPe was associated with hyperconnectivity of low-range intra-opercular connections (NBS mass 356, pFDR < 0.05), while the network linked with increased tau load in the left cerebellar DN was associated with cerebellar hyperconnectivity and cortico-cerebellar hypoconnectivity (NBS mass 517, pFDR < 0.05).

CONCLUSIONS

PSP patients show altered functional connectivity. Network incorporating deep gray matter structures demonstrate hypoconnectivity, cerebellum hyperconnectivity, while cortico-cortical connections show variable changes. Tau load in the right GPe and left DN is associated with functional networks which strengthen low-scale intra-opercular and intra-cerebellar connections and weaken opercular-cerebellar connections. These findings support the concept of tau load-dependent functional network changes in PSP, by that providing evidence for downstream effects of neuropathology on brain functionality in this primary tauopathy.

Functional connectivity in behavioral variant frontotemporal dementia

INTRODUCTION

Functional connectivity (FC)-which reflects relationships between neural activity in different brain regions-has been used to explore the functional architecture of the brain in neurodegenerative disorders. Although an increasing number of studies have explored FC changes in behavioral variant frontotemporal dementia (bvFTD), there is no focused, in-depth review about FC in bvFTD.

METHODS

Comprehensive literature search and narrative review to summarize the current field of FC in bvFTD.

RESULTS

(1) Decreased FC within the salience network (SN) is the most consistent finding in bvFTD; (2) FC changes extend beyond the SN and affect the interplay between networks; (3) results within the Default Mode Network are mixed; (4) the brain as a network is less interconnected and less efficient in bvFTD; (5) symptoms, functional impairment, and cognition are associated with FC; and (6) the functional architecture resembles patterns of neuropathological spread.

CONCLUSIONS

FC has potential as a biomarker, and future studies are expected to advance the field with multicentric initiatives, longitudinal designs, and methodological advances.

Is non-invasive brain stimulation effective for cognitive enhancement in Alzheimer's disease? An updated meta-analysis

OBJECTIVE

Alzheimer's disease dementia (AD) and its preclinical stage, mild cognitive impairment (MCI), are critical issues confronting the aging society. Non-invasive brain stimulation (NIBS) techniques have the potential to be effective tools for enhancing cognitive functioning. The main objective of our meta-analysis was to quantify and update the status of the efficacy of repetitive Transcranial Magnetic Stimulation (rTMS) and Transcranial Direct Current Stimulation (tDCS) when applied in AD and MCI.

METHODS

The systematic literature search was conducted in PubMed and Web of Science according to PRISMA statement.

RESULTS

Pooled effect sizes (Hedges' g) from 32 studies were analyzed using random effect models. We found both, rTMS and tDCS to have significant immediate cognition-enhancing effect in AD with rTMS inducing also beneficial long-term effects. We found no evidence for synergistic effect of cognitive training with NIBS.

CONCLUSIONS

In AD a clinical recommendation can be made for NEURO-AD^TM system and for high-frequency rTMS over the left dorsolateral prefrontal cortex (DLPFC) as probably effective protocols (B-level of evidence) and for anodal tDCS over the left DLPFC as a possibly effective.

SIGNIFICANCE

According to scientific literature, NIBS may be an effective method for improving cognition in AD and possibly in MCI.