Changes in dynamic transitions between integrated and segregated states underlie visual hallucinations in Parkinson's disease

Altered integrated and segregated states in cocaine use disorder

Introduction

Cocaine use disorder (CUD) is a chronic brain condition that severely impairs cognitive function and behavioral control. The neural mechanisms underlying CUD, particularly its impact on brain integration-segregation dynamics, remain unclear.

Methods

In this study, we integrate dynamic functional connectivity and graph theory to compare the brain state properties of healthy controls and CUD patients.

Results

We find that CUD influences both integrated and segregated states, leading to distinct alterations in connectivity patterns and network properties. CUD disrupts connectivity involving the default mode network, frontoparietal network, and subcortical structures. In addition, integrated states show distinct sensorimotor connectivity alterations, while segregated states exhibit significant alterations in frontoparietal-subcortical connectivity. Regional connectivity alterations among both states are significantly associated with MOR and H3 receptor distributions, with integrated states showing more receptor-connectivity couplings. Furthermore, CUD alters the positive-negative correlation balance, increases functional complexity at threshold 0, and reduces mean betweenness centrality and modularity in the critical subnetworks. Segregated states in CUD exhibit lower normalized clustering coefficients and functional complexity at a threshold of 0.3. We also identify network properties in integrated states that are reliably correlated with cocaine consumption patterns.

Discussion

Our findings reveal temporal effects of CUD on brain integration and segregation, providing novel insights into the dynamic neural mechanisms underlying cocaine addiction.

Comprehensive profiling of anaesthetised brain dynamics across phylogeny

The intrinsic dynamics of neuronal circuits shape information processing and cognitive function. Combining non-invasive neuroimaging with anaesthetic-induced suppression of information processing provides a unique opportunity to understand how local dynamics mediate the link between neurobiology and the organism's functional repertoire. To address this question, we compile a unique dataset of multi-scale neural activity during wakefulness and anesthesia encompassing human, macaque, marmoset, mouse and nematode. We then apply massive feature extraction to comprehensively characterize local neural dynamics across > 6 000 time-series features. Using dynamics as a common space for comparison across species, we identify a phylogenetically conserved dynamical profile of anaesthesia that encompasses multiple features, including reductions in intrinsic timescales. This dynamical signature has an evolutionarily conserved spatial layout, covarying with transcriptional profiles of excitatory and inhibitory neurotransmission across human, macaque and mouse cortex. At the network level, anesthetic-induced changes in local dynamics manifest as reductions in inter-regional synchrony. This relationship between local dynamics and global connectivity can be recapitulated in silico using a connectome-based computational model. Finally, this dynamical regime of anaesthesia is experimentally reversed in vivo by deep-brain stimulation of the centromedian thalamus in the macaque, resulting in restored arousal and behavioural responsiveness. Altogether, comprehensive dynamical phenotyping reveals that spatiotemporal isolation of local neural activity during anesthesia is conserved across species and anesthetics.

Benchmarking macaque brain gene expression for horizontal and vertical translation

The spatial patterning of gene expression shapes cortical organization and function. The macaque is a fundamental model organism in neuroscience, but the translational potential of macaque gene expression rests on the assumption that it is a good proxy for patterns of corresponding proteins (vertical translation) and for patterns of orthologous human genes (horizontal translation). Here, we systematically benchmark regional gene expression in macaque cortex against (i) macaque cortical receptor density and in vivo and ex vivo microstructure and (ii) human cortical gene expression. We find moderate cortex-wide correspondence between macaque gene expression and protein density, which improves by considering layer-specific gene expression. Half of the examined genes exhibit significant correlation between macaque and human across the cortex. Interspecies correspondence of gene expression is greater in unimodal than in transmodal cortex, recapitulating evolutionary cortical expansion and gene-protein correspondence in the macaque. These results showcase the potential and limitations of macaque cortical transcriptomics for translational discovery within and across species.

Motor Functional Hierarchical Organization of Cerebrum and Its Underlying Genetic Architecture in Parkinson's Disease

Hierarchy has been identified as a principle underlying the organization of human brain networks. However, it remains unclear how the network hierarchy is disrupted in Parkinson's disease (PD) motor symptoms and how it is modulated by the underlying genetic architecture. The aim of this study was to explore alterations in the motor functional hierarchical organization of the cerebrum and their underlying genetic mechanism. In this study, the brain network hierarchy of each group was described through a connectome gradient analysis among 68 healthy controls (HC), 70 postural instability and gait difficulty (PIGD) subtype, and 69 tremor-dominant (TD) subtype, including both male and female participants, according to its motor symptoms. Furthermore, transcription-neuroimaging association analyses using gene expression data from Allen Human Brain Atlas and case-control gradient differences were performed to identify genes associated with gradient alterations. Different PD motor subtypes exhibited contracted principal and secondary functional gradients relative to HC. The identified genes in different PD motor subtypes enriched for shared biological processes like metal ion transport and inorganic ion transmembrane transport. In addition, these genes were overexpressed in Ntsr+ neurons cell, enriched in extensive cortical regions and wide developmental time windows. Aberrant cerebral functional gradients in PD-related motor symptoms have been detected, and the motor-disturbed genes have shared biological functions. The present findings may contribute to a more comprehensive understanding of the molecular mechanisms underlying hierarchical alterations in PD.

Visual hallucinations in Parkinson's disease: spotlight on central cholinergic dysfunction

Visual hallucinations are a common non-motor feature of Parkinson's disease and have been associated with accelerated cognitive decline, increased mortality and early institutionalization. Despite their prevalence and negative impact on patient outcomes, the repertoire of treatments aimed at addressing this troubling symptom is limited. Over the past two decades, significant contributions have been made in uncovering the pathological and functional mechanisms of visual hallucinations, bringing us closer to the development of a comprehensive neurobiological framework. Convergent evidence now suggests that degeneration within the central cholinergic system may play a significant role in the genesis and progression of visual hallucinations. Here, we outline how cholinergic dysfunction may serve as a potential unifying neurobiological substrate underlying the multifactorial and dynamic nature of visual hallucinations. Drawing upon previous theoretical models, we explore the impact that alterations in cholinergic neurotransmission has on the core cognitive processes pertinent to abnormal perceptual experiences. We conclude by highlighting that a deeper understanding of cholinergic neurobiology and individual pathophysiology may help to improve established and emerging treatment strategies for the management of visual hallucinations and psychotic symptoms in Parkinson's disease.

Genetic and neurochemical profiles underlying cortical morphometric vulnerability to Parkinson's disease

BACKGROUND

Increasing evidence has documented cortical involvement at all stages of PD. The local vulnerabilities within certain brain regions in PD have been previously demonstrated, whereas its underlying genetic and neurochemical factors remain unclear. This study aims to investigate the spatial spectrum of cortical atrophy in Parkinson's disease (PD) and link these variances in gray matter properties and curvature respectively to putative molecular pathways and neurotransmitter factors.

METHODS

We recruited 141 clinically diagnosed PD patients and 70 healthy controls. Cortical morphological abnormalities of PD were obtained by intergroup comparisons in gray matter properties metrics and curvature measurements. Then we performed gene-category enrichment and spatial correlation analyses to evaluate the specific correspondence between cortical alteration in PD and genetic expression from the Allen Human Brain Atlas and normative neurotransmitter atlases from Neuromaps.

RESULTS

We found decreased gray matter properties in temporal, somatomotor, cingulate and occipital cortices, decreased curvature measures in occipital, temporal and orbitofrontal cortices, and increased curvature measures in somatomotor, prefrontal and posterior parietal cortices for PD patients. The related genes were enriched for the glucose metabolism, mitochondrial function, and post-translational histone modifications processes. In addition, the serotonin and norepinephrine transporter devoted more to gray matter properties alterations while the dopamine, gamma-aminobutyric acid receptors, and norepinephrine transporter were strong contributors of curvature abnormalities in PD.

CONCLUSIONS

Collectively, the present study offered interpretation of cortical morphological alterations and the cortical pathogenic theory in PD from genetic and neurochemical perspectives, which inspire further research on new pharmacotherapeutic approaches.

Development of the brain network control theory and its implications

Brain network control theory (NCT) is a groundbreaking field in neuroscience that employs system engineering and cybernetics principles to elucidate and manipulate brain dynamics. This review examined the development and applications of NCT over the past decade. We highlighted how NCT has been effectively utilized to model brain dynamics, offering new insights into cognitive control, brain development, the pathophysiology of neurological and psychiatric disorders, and neuromodulation. Additionally, we summarized the practical implementation of NCT using the nctpy package. We also presented the doubts and challenges associated with NCT and efforts made to provide better empirical validations and biological underpinnings. Finally, we outlined future directions for NCT, covering its development and applications.

Functional and connectivity correlates associated with Parkinson's disease psychosis: a systematic review

Neural underpinnings of Parkinson's disease psychosis remain unclear to this day with relatively few studies and reviews available. Using a systematic review approach, here, we aimed to qualitatively synthesize evidence from studies investigating Parkinson's psychosis-specific alterations in brain structure, function or chemistry using different neuroimaging modalities. PubMed, Web of Science and Embase databases were searched for functional MRI (task-based and resting state), diffusion tensor imaging, PET and single-photon emission computed tomography studies comparing Parkinson's disease psychosis patients with Parkinson's patients without psychosis. We report findings from 29 studies (514 Parkinson's psychosis patients, mean age ± SD = 67.92 ± 4.37 years; 51.36% males; 853 Parkinson's patients, mean age ± SD = 66.75 ± 4.19 years; 55.81% males). Qualitative synthesis revealed widespread patterns of altered brain function across task-based and resting-state functional MRI studies in Parkinson's psychosis patients compared with Parkinson's patients without psychosis. Similarly, white matter abnormalities were reported in parietal, temporal and occipital regions. Hypo-metabolism and reduced dopamine transporter binding were also reported whole brain and in sub-cortical areas. This suggests extensive alterations affecting regions involved in high-order visual processing and attentional networks.

Real-time fMRI neurofeedback modulates induced hallucinations and underlying brain mechanisms

Hallucinations can occur in the healthy population, are clinically relevant and frequent symptoms in many neuropsychiatric conditions, and have been shown to mark disease progression in patients with neurodegenerative disorders where antipsychotic treatment remains challenging. Here, we combine MR-robotics capable of inducing a clinically-relevant hallucination, with real-time fMRI neurofeedback (fMRI-NF) to train healthy individuals to up-regulate a fronto-parietal brain network associated with the robotically-induced hallucination. Over three days, participants learned to modulate occurrences of and transition probabilities to this network, leading to heightened sensitivity to induced hallucinations after training. Moreover, participants who became sensitive and succeeded in fMRI-NF training, showed sustained and specific neural changes after training, characterized by increased hallucination network occurrences during induction and decreased hallucination network occurrences during a matched control condition. These data demonstrate that fMRI-NF modulates specific hallucination network dynamics and highlights the potential of fMRI-NF as a novel antipsychotic treatment in neurodegenerative disorders and schizophrenia.

Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies

The mechanisms linking the brain's network structure to cognitively relevant activation patterns remain largely unknown. Here, by leveraging principles of network control, we show how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic database. Specifically, we systematically integrated large-scale multimodal neuroimaging data from functional magnetic resonance imaging, diffusion tractography, cortical morphometry and positron emission tomography to simulate how anatomically guided transitions between cognitive states can be reshaped by neurotransmitter engagement or by changes in cortical thickness. Our model incorporates neurotransmitter-receptor density maps (18 receptors and transporters) and maps of cortical thickness pertaining to a wide range of mental health, neurodegenerative, psychiatric and neurodevelopmental diagnostic categories (17,000 patients and 22,000 controls). The results provide a comprehensive look-up table charting how brain network organization and chemoarchitecture interact to manifest different cognitive topographies, and establish a principled foundation for the systematic identification of ways to promote selective transitions between cognitive topographies.

Machine learning for predicting cognitive decline within five years in Parkinson's disease: Comparing cognitive assessment scales with DAT SPECT and clinical biomarkers

OBJECTIVE

Parkinson's disease (PD) is an age-related neurodegenerative condition characterized mostly by motor symptoms. Although a wide range of non-motor symptoms (NMS) are frequently experienced by PD patients. One of the important and common NMS is cognitive impairment, which is measured using different cognitive scales. Monitoring cognitive impairment and its decline in PD is essential for patient care and management. In this study, our goal is to identify the most effective cognitive scale in predicting cognitive decline over a 5-year timeframe initializing clinical biomarkers and DAT SPECT.

METHODS

Machine Learning has previously shown superior performance in image and clinical data classification and detection. In this study, we propose to use machine learning with different types of data, such as DAT SPECT and clinical biomarkers, to predict PD-CD based on various cognitive scales. We collected 330 DAT SPECT images and their clinical data in baseline, years 2,3,4, and 5 from Parkinson's Progression Markers Initiative (PPMI). We then designed a 3D Autoencoder to extract deep radiomic features (DF) from DAT SPECT images, and we then concatenated it with 17 clinical features (CF) to predict cognitive decline based on Montreal Cognitive Assessment (MoCA) and The Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS-I).

RESULTS

The utilization of MoCA as a cognitive decline scale yielded better performance in various years compared to MDS-UPDRS-I. In year 4, the application of the deep radiomic feature resulted in the highest achievement, with a cross-validation AUC of 89.28, utilizing the gradient boosting classifier. For the MDS-UPDRS-I scale, the highest achievement was obtained by utilizing the deep radiomic feature, resulting in a cross-validation AUC of 81.34 with the random forest classifier.

CONCLUSIONS

The study findings indicate that the MoCA scale may be a more effective predictor of cognitive decline within 5 years compared to MDS-UPDRS-I. Furthermore, deep radiomic features had better performance compared to sole clinical biomarkers or clinical and deep radiomic combined. These results suggest that using the MoCA score and deep radiomic features extracted from DAT SPECT could be a promising approach for identifying individuals at risk for cognitive decline in four years. Future research is needed to validate these findings and explore their utility in clinical practice.

Parkinson disease psychosis: from phenomenology to neurobiological mechanisms

Parkinson disease (PD) psychosis (PDP) is a spectrum of illusions, hallucinations and delusions that are associated with PD throughout its disease course. Psychotic phenomena can manifest from the earliest stages of PD and might follow a continuum from minor hallucinations to structured hallucinations and delusions. Initially, PDP was considered to be a complication associated with dopaminergic drug use. However, subsequent research has provided evidence that PDP arises from the progression of brain alterations caused by PD itself, coupled with the use of dopaminergic drugs. The combined dysfunction of attentional control systems, sensory processing, limbic structures, the default mode network and thalamocortical connections provides a conceptual framework to explain how new incoming stimuli are incorrectly categorized, and how aberrant hierarchical predictive processing can produce false percepts that intrude into the stream of consciousness. The past decade has seen the publication of new data on the phenomenology and neurobiological basis of PDP from the initial stages of the disease, as well as the neurotransmitter systems involved in PDP initiation and progression. In this Review, we discuss the latest clinical, neuroimaging and neurochemical evidence that could aid early identification of psychotic phenomena in PD and inform the discovery of new therapeutic targets and strategies.

Functional Brain Networks of Minor and Well-Structured Major Hallucinations in Parkinson's Disease

BACKGROUND

Minor hallucinations (mHs) and well-structured major hallucinations (MHs) are common symptoms of Parkinson's disease (PD) psychosis.

OBJECTIVES

To investigate the resting-state networks (RSNs) in patients with PD without hallucinations (PD-nH), with mH (PD-mH), and with MH (PD-MH).

METHODS

A total of 73 patients with PD were enrolled (27 PD-nH, 23 PD-mH, and 23 PD-MH). Using seed-based functional connectivity analyses, we investigated the RSNs supposedly related to hallucinations in PD: the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN), ventral attention network (VAN), and visual network (VN). We compared the cognitive function and RSN connectivity among the three groups. In addition, we performed a seed-to-seed analysis to examine the inter-network connectivity within each group using the corresponding RSN seeds.

RESULTS

PD-MH group had lower test scores for attention and visuospatial functions compared with those in the other groups. The connectivity of the right intracalcarine cortex within the DAN was lower in the PD-MH group than in the others. The PD-mH and PD-MH groups showed higher connectivity in the left orbitofrontal cortex within DMN compared with the PD-nH group, whereas the connectivity was lower in the right middle frontal gyrus (MFG) within ECN, precuneus cortex within VAN, right middle temporal gyrus and precuneus cortex within DAN, and left MFG within VN. The PD-mH and PD-MH groups showed different inter-network connectivity between the five RSNs, especially regarding DAN connectivity.

CONCLUSIONS

DAN dysfunction may be a key factor in the progression from mH to MH in patients with PD. © 2023 International Parkinson and Movement Disorder Society.

Indirect evidence for altered dopaminergic neurotransmission in very premature-born adults

While animal models indicate altered brain dopaminergic neurotransmission after premature birth, corresponding evidence in humans is scarce due to missing molecular imaging studies. To overcome this limitation, we studied dopaminergic neurotransmission changes in human prematurity indirectly by evaluating the spatial co-localization of regional alterations in blood oxygenation fluctuations with the distribution of adult dopaminergic neurotransmission. The study cohort comprised 99 very premature-born (<32 weeks of gestation and/or birth weight below 1500 g) and 107 full-term born young adults, being assessed by resting-state functional MRI (rs-fMRI) and IQ testing. Normative molecular imaging dopamine neurotransmission maps were derived from independent healthy control groups. We computed the co-localization of local (rs-fMRI) activity alterations in premature-born adults with respect to term-born individuals to different measures of dopaminergic neurotransmission. We performed selectivity analyses regarding other neuromodulatory systems and MRI measures. In addition, we tested if the strength of the co-localization is related to perinatal measures and IQ. We found selectively altered co-localization of rs-fMRI activity in the premature-born cohort with dopamine-2/3-receptor availability in premature-born adults. Alterations were specific for the dopaminergic system but not for the used MRI measure. The strength of the co-localization was negatively correlated with IQ. In line with animal studies, our findings support the notion of altered dopaminergic neurotransmission in prematurity which is associated with cognitive performance.

Structural and Functional Imaging Correlates of Visual Hallucinations in Parkinson's Disease

PURPOSE OF REVIEW

To review recent structural and functional MRI studies of visual hallucinations in Parkinson's disease.

RECENT FINDINGS

Previously, neuroimaging had shown inconsistent findings in patients with Parkinson's hallucinations, especially in studies examining grey matter volume. However, recent advances in structural and functional MRI techniques allow better estimates of structural connections, as well as the direction of connectivity in functional MRI. These provide more sensitive measures of changes in structural connectivity and allow models of the changes in directional functional connectivity to be tested. We identified 27 relevant studies and found that grey matter imaging continues to show heterogeneous findings in Parkinson's patients with visual hallucinations. Newer approaches in diffusion imaging and functional MRI are consistent with emerging models of Parkinson's hallucinations, suggesting shifts in attentional networks. In particular, reduced bottom-up, incoming sensory information, and over-weighting of top-down signals appear to be important drivers of visual hallucinations in Parkinson's disease.

Reduced emergent character of neural dynamics in patients with a disrupted connectome

High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as "Integrated Information Decomposition," which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems - including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients' structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be related to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence.

Transitions between cognitive topographies: contributions of network structure, neuromodulation, and disease

Patterns of neural activity underlie human cognition. Transitions between these patterns are orchestrated by the brain's network architecture. What are the mechanisms linking network structure to cognitively relevant activation patterns? Here we implement principles of network control to investigate how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic engine. We also systematically incorporate neurotransmitter receptor density maps (18 receptors and transporters) and disease-related cortical abnormality maps (11 neurodegenerative, psychiatric and neurodevelopmental diseases; N = 17 000 patients, N = 22 000 controls). Integrating large-scale multimodal neuroimaging data from functional MRI, diffusion tractography, cortical morphometry, and positron emission tomography, we simulate how anatomically-guided transitions between cognitive states can be reshaped by pharmacological or pathological perturbation. Our results provide a comprehensive look-up table charting how brain network organisation and chemoarchitecture interact to manifest different cognitive topographies. This computational framework establishes a principled foundation for systematically identifying novel ways to promote selective transitions between desired cognitive topographies.