Dynamic causal modelling of effective connectivity from fMRI: are results reproducible and sensitive to Parkinson's disease and its treatment?

LRE-MMF: A novel multi-modal fusion algorithm for detecting neurodegeneration in Parkinson's disease among the geriatric population

Parkinson's disease (PD) is a prevalent neurological disorder characterized by progressive dopaminergic neuron loss, leading to both motor and non-motor symptoms. Early and accurate diagnosis is challenging due to the subtle and variable nature of early symptoms. This study aims to address these diagnostic challenges by proposing a novel method, Localized Region Extraction and Multi-Modal Fusion (LRE-MMF), designed to enhance diagnostic accuracy through the integration of structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data. The LRE-MMF method utilizes the complementary strengths of sMRI and rs-fMRI: sMRI provides detailed anatomical information, while rs-fMRI captures functional connectivity patterns. We applied this approach to a dataset consisting of 20 PD patients and 20 healthy controls (HC), all scanned with a 3 T MRI. The primary objective was to determine whether the integration of sMRI and rs-fMRI through the LRE-MMF method improves the classification accuracy between PD and HC subjects. LRE-MMF involves the division of imaging data into localized regions, followed by feature extraction and dimensionality reduction using Principal Component Analysis (PCA). The resulting features were fused and processed through a neural network to learn high-level representations. The model achieved an accuracy of 75 %, with a precision of 0.8125, recall of 0.65, and an AUC of 0.8875. The validation accuracy curves indicated good generalization, with significant brain regions identified, including the caudate, putamen, thalamus, supplementary motor area, and precuneus, as per the AAL atlas. These results demonstrate the potential of the LRE-MMF method for improving early diagnosis and understanding of PD by effectively utilizing both sMRI and rs-fMRI data. This approach could contribute to the development of more accurate diagnostic tools.

Reliability of dynamic causal modelling of resting-state magnetoencephalography

This study assesses the reliability of resting-state dynamic causal modelling (DCM) of magnetoencephalography (MEG) under conductance-based canonical microcircuit models, in terms of both posterior parameter estimates and model evidence. We use resting-state MEG data from two sessions, acquired 2 weeks apart, from a cohort with high between-subject variance arising from Alzheimer's disease. Our focus is not on the effect of disease, but on the reliability of the methods (as within-subject between-session agreement), which is crucial for future studies of disease progression and drug intervention. To assess the reliability of first-level DCMs, we compare model evidence associated with the covariance among subject-specific free energies (i.e., the 'quality' of the models) with versus without interclass correlations. We then used parametric empirical Bayes (PEB) to investigate the differences between the inferred DCM parameter probability distributions at the between subject level. Specifically, we examined the evidence for or against parameter differences (i) within-subject, within-session, and between-epochs; (ii) within-subject between-session; and (iii) within-site between-subjects, accommodating the conditional dependency among parameter estimates. We show that for data acquired close in time, and under similar circumstances, more than 95% of inferred DCM parameters are unlikely to differ, speaking to mutual predictability over sessions. Using PEB, we show a reciprocal relationship between a conventional definition of 'reliability' and the conditional dependency among inferred model parameters. Our analyses confirm the reliability and reproducibility of the conductance-based DCMs for resting-state neurophysiological data. In this respect, the implicit generative modelling is suitable for interventional and longitudinal studies of neurological and psychiatric disorders.

Motor network dynamic resting state fMRI connectivity of neurotypical children in regions affected by cerebral palsy

Background

Normative childhood motor network resting-state fMRI effective connectivity is undefined, yet necessary for translatable dynamic resting-state-network-informed evaluation in pediatric cerebral palsy.

Methods

Cross-spectral dynamic causal modeling of resting-state-fMRI was investigated in 50 neurotypically developing 5- to 13-year-old children. Fully connected six-node network models per hemisphere included primary motor cortex, striatum, subthalamic nucleus, globus pallidus internus, thalamus, and contralateral cerebellum. Parametric Empirical Bayes with exhaustive Bayesian model reduction and Bayesian modeling averaging informed the model; Purdue Pegboard Test scores of hand motor behavior were the covariate at the group level to determine the effective-connectivity-functional behavior relationship.

Results

Although both hemispheres exhibited similar effective connectivity of motor cortico-basal ganglia-cerebellar networks, magnitudes were slightly greater on the right, except for left-sided connections of the striatum which were more numerous and of opposite polarity. Inter-nodal motor network effective connectivity remained consistent and robust across subjects. Age had a greater impact on connections to the contralateral cerebellum, bilaterally. Motor behavior, however, affected different connections in each hemisphere, exerting a more prominent effect on the left modulatory connections to the subthalamic nucleus, contralateral cerebellum, primary motor cortex, and thalamus.

Discussion

This study revealed a consistent pattern of directed resting-state effective connectivity in healthy children aged 5-13 years within the motor network, encompassing cortical, subcortical, and cerebellar regions, correlated with motor skill proficiency. Both hemispheres exhibited similar effective connectivity within motor cortico-basal ganglia-cerebellar networks reflecting inter-nodal signal direction predicted by other modalities, mainly differing from task-dependent studies due to network differences at rest. Notably, age-related changes were more pronounced in connections to the contralateral cerebellum. Conversely, motor behavior distinctly impacted connections in each hemisphere, emphasizing its role in modulating left sided connections to the subthalamic nucleus, contralateral cerebellum, primary motor cortex, and thalamus. Motor network effective connectivity was correlated with motor behavior, validating its physiological significance. This study is the first to evaluate a normative effective connectivity model for the pediatric motor network using resting-state functional MRI correlating with behavior and serves as a foundation for identifying abnormal findings and optimizing targeted interventions like deep brain stimulation, potentially influencing future therapeutic approaches for children with movement disorders.

Parkinson's disease impairs cortical sensori-motor decision-making cascades

The transformation from perception to action requires a set of neuronal decisions about the nature of the percept, identification and selection of response options and execution of the appropriate motor response. The unfolding of such decisions is mediated by distributed representations of the decision variables-evidence and intentions-that are represented through oscillatory activity across the cortex. Here we combine magneto-electroencephalography and linear ballistic accumulator models of decision-making to reveal the impact of Parkinson's disease during the selection and execution of action. We used a visuomotor task in which we independently manipulated uncertainty in sensory and action domains. A generative accumulator model was optimized to single-trial neurophysiological correlates of human behaviour, mapping the cortical oscillatory signatures of decision-making, and relating these to separate processes accumulating sensory evidence and selecting a motor action. We confirmed the role of widespread beta oscillatory activity in shaping the feed-forward cascade of evidence accumulation from resolution of sensory inputs to selection of appropriate responses. By contrasting the spatiotemporal dynamics of evidence accumulation in age-matched healthy controls and people with Parkinson's disease, we identified disruption of the beta-mediated cascade of evidence accumulation as the hallmark of atypical decision-making in Parkinson's disease. In frontal cortical regions, there was inefficient processing and transfer of perceptual information. Our findings emphasize the intimate connection between abnormal visuomotor function and pathological oscillatory activity in neurodegenerative disease. We propose that disruption of the oscillatory mechanisms governing fast and precise information exchanges between the sensory and motor systems contributes to behavioural changes in people with Parkinson's disease.

Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy

We present a hierarchical empirical Bayesian framework for testing hypotheses about neurotransmitters' concertation as empirical prior for synaptic physiology using ultra-high field magnetic resonance spectroscopy (7T-MRS) and magnetoencephalography data (MEG). A first level dynamic causal modelling of cortical microcircuits is used to infer the connectivity parameters of a generative model of individuals' neurophysiological observations. At the second level, individuals' 7T-MRS estimates of regional neurotransmitter concentration supply empirical priors on synaptic connectivity. We compare the group-wise evidence for alternative empirical priors, defined by monotonic functions of spectroscopic estimates, on subsets of synaptic connections. For efficiency and reproducibility, we used Bayesian model reduction (BMR), parametric empirical Bayes and variational Bayesian inversion. In particular, we used Bayesian model reduction to compare alternative model evidence of how spectroscopic neurotransmitter measures inform estimates of synaptic connectivity. This identifies the subset of synaptic connections that are influenced by individual differences in neurotransmitter levels, as measured by 7T-MRS. We demonstrate the method using resting-state MEG (i.e., task-free recording) and 7T-MRS data from healthy adults. Our results confirm the hypotheses that GABA concentration influences local recurrent inhibitory intrinsic connectivity in deep and superficial cortical layers, while glutamate influences the excitatory connections between superficial and deep layers and connections from superficial to inhibitory interneurons. Using within-subject split-sampling of the MEG dataset (i.e., validation by means of a held-out dataset), we show that model comparison for hypothesis testing can be highly reliable. The method is suitable for applications with magnetoencephalography or electroencephalography, and is well-suited to reveal the mechanisms of neurological and psychiatric disorders, including responses to psychopharmacological interventions.

Comparative efficacy of transcranial magnetic stimulation on different targets in Parkinson's disease: A Bayesian network meta-analysis

Background/Objective

The efficacy of transcranial magnetic stimulation (TMS) on Parkinson's disease (PD) varies across the stimulation targets. This study aims to estimate the effect of different TMS targets on motor symptoms in PD.

Methods

A Bayesian hierarchical model was built to assess the effects across different TMS targets, and the rank probabilities and the surface under the cumulative ranking curve (SUCRA) values were calculated to determine the ranks of each target. The primary outcome was the Unified Parkinson's Disease Rating Scale part-III. Inconsistency between direct and indirect comparisons was assessed using the node-splitting method.

Results

Thirty-six trials with 1,122 subjects were included for analysis. The pair-wise meta-analysis results showed that TMS could significantly improve motor symptoms in PD patients. Network meta-analysis results showed that the high-frequency stimulation over bilateral M1, bilateral DLPFC, and M1+DLPFC could significantly reduce the UPDRS-III scores compared with sham conditions. The high-frequency stimulation over both M1 and DLPFC had a more significant effect when compared with other parameters, and ranked first with the highest SCURA value. There was no significant inconsistency between direct and indirect comparisons.

Conclusion

Considering all settings reported in our research, high-frequency stimulation over bilateral M1 or bilateral DLPFC has a moderate beneficial effect on the improvement of motor symptoms in PD (high confidence rating). High-frequency stimulation over M1+DLPFC has a prominent beneficial effect and appears to be the most effective TMS parameter setting for ameliorating motor symptoms of PD patients (high confidence rating).

Computational psychiatry: from synapses to sentience

This review considers computational psychiatry from a particular viewpoint: namely, a commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion of a generative model as underwriting (i) sentient processing in the brain, and (ii) the scientific process in psychiatry. The story starts with a view of the brain-from cognitive and computational neuroscience-as an organ of inference and prediction. This offers a formal description of neuronal message passing, distributed processing and belief propagation in neuronal networks; and how certain kinds of dysconnection lead to aberrant belief updating and false inference. The dysconnections in question can be read as a pernicious synaptopathy that fits comfortably with formal notions of how we-or our brains-encode uncertainty or its complement, precision. It then considers how the ensuing process theories are tested empirically, with an emphasis on the computational modelling of neuronal circuits and synaptic gain control that mediates attentional set, active inference, learning and planning. The opportunities afforded by this sort of modelling are considered in light of in silico experiments; namely, computational neuropsychology, computational phenotyping and the promises of a computational nosology for psychiatry. The resulting survey of computational approaches is not scholarly or exhaustive. Rather, its aim is to review a theoretical narrative that is emerging across subdisciplines within psychiatry and empirical scales of investigation. These range from epilepsy research to neurodegenerative disorders; from post-traumatic stress disorder to the management of chronic pain, from schizophrenia to functional medical symptoms.

Effects of face repetition on ventral visual stream connectivity using dynamic causal modelling of fMRI data

Stimulus repetition normally causes reduced neural activity in brain regions that process that stimulus. Some theories claim that this "repetition suppression" reflects local mechanisms such as neuronal fatigue or sharpening within a region, whereas other theories claim that it results from changed connectivity between regions, following changes in synchrony or top-down predictions. In this study, we applied dynamic causal modeling (DCM) on a public fMRI dataset involving repeated presentations of faces and scrambled faces to test whether repetition affected local (self-connections) and/or between-region connectivity in left and right early visual cortex (EVC), occipital face area (OFA) and fusiform face area (FFA). Face "perception" (faces versus scrambled faces) modulated nearly all connections, within and between regions, including direct connections from EVC to FFA, supporting a non-hierarchical view of face processing. Face "recognition" (familiar versus unfamiliar faces) modulated connections between EVC and OFA/FFA, particularly in the left hemisphere. Most importantly, immediate and delayed repetition of stimuli were also best captured by modulations of connections between EVC and OFA/FFA, but not self-connections of OFA/FFA, consistent with synchronization or predictive coding theories, though also possibly reflecting local mechanisms like synaptic depression.

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.

Dynamic Causal Modeling of the Prefrontal/Amygdala Network During Processing of Emotional Faces

Introduction: The importance of the amygdala/medial orbitofrontal cortex (OFC) network during processing of emotional stimuli, emotional faces in particular, is well established. This premise is supported by converging evidence from animal models, human neuroanatomical results, and neuroimaging studies. However, there is missing evidence from human brain connectivity studies that the OFC and no other prefrontal brain areas such as the dorsolateral prefrontal cortex (DLPFC) or ventrolateral prefrontal cortex (VLPFC) are responsible for amygdala regulation in the functional context of emotional face stimuli. Methods: Dynamic causal modeling of ultrahigh-field functional magnetic resonance imaging data acquired at 7 Tesla in 38 healthy subjects and a well-established paradigm for emotional face processing were used to assess the central role of the OFC to provide empirical validation for the assumed network architecture. Results: Using Bayesian model selection, it is demonstrated that indeed the OFC, and not the VLPFC and the DLPFC, downregulates amygdala activation during the emotion discrimination task. In addition, Bayesian model averaging group results were rigorously tested using bootstrapping, further corroborating these findings and providing an estimator for robustness and optimal sample sizes. Discussion: While it is true that VLPFC and DLPFC are relevant for the processing of emotional faces and are connected to the OFC, the OFC appears to be a central hub for the prefrontal/amygdala interaction. Impact statement Using dynamic causal modeling (DCM), abnormal effective connectivity in the orbitofrontal cortex (OFC)/amygdala network has been repeatedly observed in the pathophysiology of psychiatric disorders. However, it has to be considered that these findings are all based on the a priori assumption of the OFC being the central area for prefrontal control regulating amygdala activation. This is particularly important, as DCM results conditionally depend on the underlying model space used for model selection. Using Bayesian model comparison methods, it is shown that the OFC (and not the dorsolateral prefrontal cortex or ventrolateral prefrontal cortex) engages in amygdala downregulation in the context emotional face processing.

Motor imagery in amyotrophic lateral Sclerosis: An fMRI study of postural control

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ALS is associated with postural control impairment.

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DCM and PEB frameworks help to characterize connectivity patterns during gait.

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Clinical manifestations of ALS are underpinned by selective network dysfunction.

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Altered BG-SMA and SMA-PPC connectivity are observed during imagined gait in ALS.

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Enhanced BG-cerebellar connectivity may represent functional adaptation.

Background

The functional reorganization of brain networks sustaining gait is poorly characterized in amyotrophic lateral sclerosis (ALS) despite ample evidence of progressive disconnection between brain regions. The main objective of this fMRI study is to assess gait imagery-specific networks in ALS patients using dynamic causal modeling (DCM) complemented by parametric empirical Bayes (PEB) framework.

Method

Seventeen lower motor neuron predominant (LMNp) ALS patients, fourteen upper motor neuron predominant (UMNp) ALS patients and fourteen healthy controls participated in this study. Each subject performed a dual motor imagery task: normal and precision gait. The Movement Imagery Questionnaire (MIQ-rs) and imagery time (IT) were used to evaluate gait imagery in each participant. In a neurobiological computational model, the circuits involved in imagined gait and postural control were investigated by modelling the relationship between normal/precision gait and connection strengths.

Results

Behavioral results showed significant increase in IT in UMNp patients compared to healthy controls (P_corrected < 0.05) and LMNp (P_corrected < 0.05). During precision gait, healthy controls activate the model's circuits involved in the imagined gait and postural control. In UMNp, decreased connectivity (inhibition) from basal ganglia (BG) to supplementary motor area (SMA) and from SMA to posterior parietal cortex (PPC) is observed. Contrary to healthy controls, DCM detects no cerebellar-PPC connectivity in neither UMNp nor LMNp ALS. During precision gait, bilateral connectivity (excitability) between SMA and BG is observed in the LMNp group contrary to UMNp and healthy controls.

Conclusions

Our findings demonstrate the utility of implementing both DCM and PEB to characterize connectivity patterns in specific patient phenotypes. Our approach enables the identification of specific circuits involved in postural deficits, and our findings suggest a putative excitatory–inhibitory imbalance. More broadly, our data demonstrate how clinical manifestations are underpinned by network-specific disconnection phenomena in ALS.

Is Resting State Functional MRI Effective Connectivity in Movement Disorders Helpful? A Focused Review Across Lifespan and Disease

In the evolving modern era of neuromodulation for movement disorders in adults and children, much progress has been made recently characterizing the human motor network (MN) with potentially important treatment implications. Herein is a focused review of relevant resting state fMRI functional and effective connectivity of the human motor network across the lifespan in health and disease. The goal is to examine how the transition from functional connectivity to dynamic effective connectivity may be especially informative of network-targeted movement disorder therapies, with hopeful implications for children.

Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by topological abnormalities in large-scale functional brain networks, which are commonly analyzed using undirected correlations in the activation signals between brain regions. This approach assumes simultaneous activation of brain regions, despite previous evidence showing that brain activation entails causality, with signals being typically generated in one region and then propagated to other ones. To address this limitation, here, we developed a new method to assess whole-brain directed functional connectivity in participants with PD and healthy controls using antisymmetric delayed correlations, which capture better this underlying causality. Our results show that whole-brain directed connectivity, computed on functional magnetic resonance imaging data, identifies widespread differences in the functional networks of PD participants compared with controls, in contrast to undirected methods. These differences are characterized by increased global efficiency, clustering, and transitivity combined with lower modularity. Moreover, directed connectivity patterns in the precuneus, thalamus, and cerebellum were associated with motor, executive, and memory deficits in PD participants. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network differences occurring in PD compared with standard methods, opening new opportunities for brain connectivity analysis and development of new markers to track PD progression.

Test-retest reliability of regression dynamic causal modeling

Regression dynamic causal modeling (rDCM) is a novel and computationally highly efficient method for inferring effective connectivity at the whole-brain level. While face and construct validity of rDCM have already been demonstrated, here we assessed its test-retest reliability—a test-theoretical property of particular importance for clinical applications—together with group-level consistency of connection-specific estimates and consistency of whole-brain connectivity patterns over sessions. Using the Human Connectome Project dataset for eight different paradigms (tasks and rest) and two different parcellation schemes, we found that rDCM provided highly consistent connectivity estimates at the group level across sessions. Second, while test-retest reliability was limited when averaging over all connections (range of mean intraclass correlation coefficient 0.24–0.42 over tasks), reliability increased with connection strength, with stronger connections showing good to excellent test-retest reliability. Third, whole-brain connectivity patterns by rDCM allowed for identifying individual participants with high (and in some cases perfect) accuracy. Comparing the test-retest reliability of rDCM connectivity estimates with measures of functional connectivity, rDCM performed favorably—particularly when focusing on strong connections. Generally, for all methods and metrics, task-based connectivity estimates showed greater reliability than those from the resting state. Our results underscore the potential of rDCM for human connectomics and clinical applications.

Effective connectivity in subcortical visual structures in de novo Patients with Parkinson's Disease

BACKGROUND

Parkinson's disease (PD) manifests with the appearance of non-motor symptoms before motor symptoms onset. Among these, dysfunctioning visual structures have recently been reported to occur at early disease stages.

OBJECTIVE

This study addresses effective connectivity in the visual network of PD patients.

METHODS

Using functional MRI and dynamic causal modeling analysis, we evaluated the connectivity between the superior colliculus, the lateral geniculate nucleus and the primary visual area V1 in de novo untreated PD patients (n = 22). A subset of the PD patients (n = 8) was longitudinally assessed two times at two months and at six months after starting dopaminergic treatment. Results were compared to those of age-matched healthy controls (n = 22).

RESULTS

Our results indicate that the superior colliculus drives cerebral activity for luminance contrast processing both in healthy controls and untreated PD patients. The same effective connectivity was observed with neuromodulatory differences in terms of neuronal dynamic interactions. Our main findings were that the modulation induced by luminance contrast changes of the superior colliculus connectivity (self-connectivity and connectivity to the lateral geniculate nucleus) was inhibited in PD patients (effect of contrast: p = 0.79 and p = 0.77 respectively). The introduction of dopaminergic medication in a subset (n = 8) of the PD patients failed to restore the effective connectivity modulation observed in the healthy controls.

INTERPRETATION

The deficits in luminance contrast processing in PD was associated with a deficiency in connectivity adjustment from the superior colliculus to the lateral geniculate nucleus and to V1. No differences in cerebral blood flow were observed between controls and PD patients suggesting that the deficiency was at the neuronal level. Administration of a dopaminergic treatment over six months was not able to normalize the observed alterations in inter-regional coupling. These findings highlight the presence of early dysfunctions in primary visual areas, which might be used as early markers of the disease.

The neurophysiological architecture of semantic dementia: spectral dynamic causal modelling of a neurodegenerative proteinopathy

The selective destruction of large-scale brain networks by pathogenic protein spread is a ubiquitous theme in neurodegenerative disease. Characterising the circuit architecture of these diseases could illuminate both their pathophysiology and the computational architecture of the cognitive processes they target. However, this is challenging using standard neuroimaging techniques. Here we addressed this issue using a novel technique-spectral dynamic causal modelling-that estimates the effective connectivity between brain regions from resting-state fMRI data. We studied patients with semantic dementia-the paradigmatic disorder of the brain system mediating world knowledge-relative to healthy older individuals. We assessed how the effective connectivity of the semantic appraisal network targeted by this disease was modulated by pathogenic protein deposition and by two key phenotypic factors, semantic impairment and behavioural disinhibition. The presence of pathogenic protein in SD weakened the normal inhibitory self-coupling of network hubs in both antero-mesial temporal lobes, with development of an abnormal excitatory fronto-temporal projection in the left cerebral hemisphere. Semantic impairment and social disinhibition were linked to a similar but more extensive profile of abnormally attenuated inhibitory self-coupling within temporal lobe regions and excitatory projections between temporal and inferior frontal regions. Our findings demonstrate that population-level dynamic causal modelling can disclose a core pathophysiological feature of proteinopathic network architecture-attenuation of inhibitory connectivity-and the key elements of distributed neuronal processing that underwrite semantic memory.

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.

Cerebellar-cortical function and connectivity during sensorimotor behavior in aging FMR1 gene premutation carriers

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FMR1 premutation carriers show increased variability in motor control.

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Premutation carriers show reduced extrastriate activation during motor behavior.

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Premutation carriers show reduced extrastriate-cerebellar functional connectivity.

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Reduced extrastriate-cerebellar functional connectivity is related to motor issues.

Introduction

Premutation carriers of the FMR1 gene are at risk of developing fragile X-associated tremor/ataxia syndrome (FXTAS), a neurodegenerative disease characterized by motor, cognitive, and psychiatric decline as well as cerebellar and cerebral white matter pathology. Several studies have documented preclinical sensorimotor issues in aging premutation carriers, but the extent to which sensorimotor brain systems are affected and may represent early indicators of atypical neurodegeneration has not been determined.

Materials and methods

Eighteen healthy controls and 16 FMR1 premutation carriers (including five with possible, probable, or definite FXTAS) group-matched on age, sex, and handedness completed a visually guided precision gripping task with their right hand during fMRI. During the test, they used a modified pinch grip to press at 60% of their maximum force against a custom fiber-optic transducer. Participants viewed a horizontal white force bar that moved upward with increased force and downward with decreased force and a static target bar that was red during rest and turned green to cue the participant to begin pressing at the beginning of each trial. Participants were instructed to press so that the white force bar stayed as steady as possible at the level of the green target bar. Trials were 2-sec in duration and alternated with 2-sec rest periods. Five 24-sec blocks consisting of six trials were presented. Participants’ reaction time, the accuracy of their force relative to the target force, and the variability of their force accuracy across trials were examined. BOLD signal change and task-based functional connectivity (FC) were examined during force vs. rest.

Results

Relative to healthy controls, premutation carriers showed increased trial-to-trial variability of force output, though this was specific to younger premutation carriers in our sample. Relative to healthy controls, premutation carriers also showed reduced extrastriate activation during force relative to rest. FC between ipsilateral cerebellar Crus I and extrastriate cortex was reduced in premutation carriers compared to controls. Reduced Crus I-extrastriate FC was related to increased force accuracy variability in premutation carriers. Increased reaction time was associated with more severe clinically rated neurological abnormalities.

Conclusions

Findings of reduced activation in extrastriate cortex and reduced Crus I-extrastriate FC implicate deficient visual feedback processing and reduced cerebellar modulation of corrective motor commands. Our results are consistent with documented cerebellar pathology and visual-spatial processing in FXTAS and pre-symptomatic premutation carriers, and suggest FC alterations of cerebellar-cortical networks during sensorimotor behavior may represent a “prodromal” feature associated with FXTAS degeneration.

GABA-ergic Dynamics in Human Frontotemporal Networks Confirmed by Pharmaco-Magnetoencephalography

To bridge the gap between preclinical cellular models of disease and in vivo imaging of human cognitive network dynamics, there is a pressing need for informative biophysical models. Here we assess dynamic causal models (DCM) of cortical network responses, as generative models of magnetoencephalographic observations during an auditory oddball roving paradigm in healthy adults. This paradigm induces robust perturbations that permeate frontotemporal networks, including an evoked 'mismatch negativity' response and transiently induced oscillations. Here, we probe GABAergic influences in the networks using double-blind placebo-controlled randomized-crossover administration of the GABA reuptake inhibitor, tiagabine (oral, 10 mg) in healthy older adults. We demonstrate the facility of conductance-based neural mass mean-field models, incorporating local synaptic connectivity, to investigate laminar-specific and GABAergic mechanisms of the auditory response. The neuronal model accurately recapitulated the observed magnetoencephalographic data. Using parametric empirical Bayes for optimal model inversion across both drug sessions, we identify the effect of tiagabine on GABAergic modulation of deep pyramidal and interneuronal cell populations. We found a transition of the main GABAergic drug effects from auditory cortex in standard trials to prefrontal cortex in deviant trials. The successful integration of pharmaco- magnetoencephalography with dynamic causal models of frontotemporal networks provides a potential platform on which to evaluate the effects of disease and pharmacological interventions.SIGNIFICANCE STATEMENT Understanding human brain function and developing new treatments require good models of brain function. We tested a detailed generative model of cortical microcircuits that accurately reproduced human magnetoencephalography, to quantify network dynamics and connectivity in frontotemporal cortex. This approach identified the effect of a test drug (GABA-reuptake inhibitor, tiagabine) on neuronal function (GABA-ergic dynamics), opening the way for psychopharmacological studies in health and disease with the mechanistic precision afforded by generative models of the brain.

fMRI in Non-human Primate: A Review on Factors That Can Affect Interpretation and Dynamic Causal Modeling Application

Dynamic causal modeling (DCM)-a framework for inferring hidden neuronal states from brain activity measurements (e. g., fMRI) and their context-dependent modulation-was developed for human neuroimaging, and has not been optimized for non-human primate (NHP) studies, which are usually done under anesthesia. Animal neuroimaging studies offer the potential to improve effective connectivity modeling using DCM through combining functional imaging with invasive procedures such as in vivo optogenetic or electrical stimulation. Employing a Bayesian approach, model parameters are estimated based on prior knowledge of conditions that might be related to neural and BOLD dynamics (e.g., requires empirical knowledge about the range of plausible parameter values). As such, we address the following questions in this review: What factors need to be considered when applying DCM to NHP data? What differences in functional networks, cerebrovascular architecture and physiology exist between human and NHPs that are relevant for DCM application? How do anesthetics affect vascular physiology, BOLD contrast, and neural dynamics-particularly, effective communication within, and between networks? Considering the factors that are relevant for DCM application to NHP neuroimaging, we propose a strategy for modeling effective connectivity under anesthesia using an integrated physiologic-stochastic DCM (IPS-DCM).

The Future of Brain Imaging in Parkinson's Disease

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that is associated with distinct abnormalities in brain function and structure. Here we discuss how future developments in functional, structural and nuclear brain imaging may help us to better understand, diagnose, and potentially even treat PD. These new horizons may be reached by developing tracers that specifically bind to alpha synuclein, by looking into different places in the body (such as the gut) or in smaller cerebral nuclei (with improved spatial resolution), and by developing new approaches for quantifying and interpreting altered dynamics in large-scale brain networks.

Dopaminergic modulation of motor network compensatory mechanisms in Parkinson's disease

The dopaminergic system has a unique gating function in the initiation and execution of movements. When the interhemispheric imbalance of dopamine inherent to the healthy brain is disrupted, as in Parkinson's disease (PD), compensatory mechanisms act to stave off behavioral changes. It has been proposed that two such compensatory mechanisms may be (a) a decrease in motor lateralization, observed in drug-naïve PD patients and (b) reduced inhibition - increased facilitation. Seeking to investigate the differential effect of dopamine depletion and subsequent substitution on compensatory mechanisms in non-drug-naïve PD, we studied 10 PD patients and 16 healthy controls, with patients undergoing two test sessions - "ON" and "OFF" medication. Using a simple visually-cued motor response task and fMRI, we investigated cortical motor activation - in terms of laterality, contra- and ipsilateral percent BOLD signal change and effective connectivity in the parametric empirical Bayes framework. We found that decreased motor lateralization persists in non-drug-naïve PD and is concurrent with decreased contralateral activation in the cortical motor network. Normal lateralization is not reinstated by dopamine substitution. In terms of effective connectivity, disease-related changes primarily affect ipsilaterally-lateralized homotopic cortical motor connections, while medication-related changes affect contralaterally-lateralized homotopic connections. Our findings suggest that, in non-drug-naïve PD, decreased lateralization is no longer an adaptive cortical mechanism, but rather the result of maladaptive changes, related to disease progression and long-term dopamine replacement. These findings highlight the need for the development of noninvasive therapies, which would promote the adaptive mechanisms of the PD brain.

Age-related differences in the structural and effective connectivity of cognitive control: a combined fMRI and DTI study of mental arithmetic

Cognitive changes with aging are highly variable across individuals. This study investigated whether cognitive control performance might depend on preservation of structural and effective connectivity in older individuals. Specifically, we tested inhibition following working memory (WM) updating and maintenance. We analyzed diffusion tensor imaging and functional magnetic resonance imaging data in thirty-four young adults and thirty-four older adults, who performed an arithmetic verification task during functional magnetic resonance imaging. Results revealed larger arithmetic interference in older adults relative to young adults after WM updating, whereas both groups showed similar interference after WM maintenance. In both groups, arithmetic interference was associated with larger activations and stronger effective connectivity among bilateral anterior cingulate, bilateral inferior frontal gyrus, and left angular gyrus, with larger activations of frontal regions in older adults than in younger adults. In older adults, preservation of frontoparietal structural microstructure, especially involving the inferior frontaloccipital fasciculus, was associated with reduced interference, and stronger task-related effective connectivity. These results highlight how both structural and functional changes in the cognitive control network contribute to individual variability in performance during aging.

Changes in Centrality Frequency of the Default Mode Network in Individuals With Subjective Cognitive Decline

Despite subjective cognitive decline (SCD), a preclinical stage of Alzheimer's disease (AD), being widely studied in recent years, studies on centrality frequency in individuals with SCD are lacking. This study aimed to investigate the differences in centrality frequency between individuals with SCD and normal controls (NCs). Forty individuals with SCD and 53 well-matched NCs underwent a resting-state functional magnetic resonance imaging scan. We assessed individual dynamic functional connectivity using sliding window correlations. In each time window, brain regions with a high degree centrality were defined as hubs. Across the entire time window, the proportion of time that the hub appeared was characterized as centrality frequency. The centrality frequency correlated with cognitive performance differently in individuals with SCD and NCs. Our results revealed that in individuals with SCD, compared with NCs, correlations between centrality frequency of the anterior cortical regions and cognitive performance decreased (79.2% for NCs and 43.5% for individuals with SCD). In contrast, correlations between centrality frequency of the posterior cortical regions and cognitive performance increased in SCD individuals compared with NCs (20.8% for NCs and 56.5% for individuals with SCD). Moreover, the changes mainly focused on the anterior (93.3% for NCs and 45.5% for individuals with SCD) and posterior (6.7% for NCs and 54.5% for individuals with SCD) regions associated with the default mode network (DMN). In addition, we used absolute thresholds (correlation efficient r = 0.2, 0.25) and proportional thresholds (sparsity = 0.2, 0.25) to verify the results. Dynamic results are relative stable at absolute thresholds while static results are relative stable at proportional thresholds. Converging findings provide a new framework for the detection of the changes occurring in individuals with SCD via centrality frequency of the DMN.

A Bayesian Account of the Sensory-Motor Interactions Underlying Symptoms of Tourette Syndrome

Tourette syndrome is a hyperkinetic movement disorder. Characteristic features include tics, recurrent movements that are experienced as compulsive and "unwilled"; uncomfortable premonitory sensations that resolve through tic release; and often, the ability to suppress tics temporarily. We demonstrate how these symptoms and features can be understood in terms of aberrant predictive (Bayesian) processing in hierarchical neural systems, explaining specifically: why tics arise, their "unvoluntary" nature, how premonitory sensations emerge, and why tic suppression works-sometimes. In our model, premonitory sensations and tics are generated through over-precise priors for sensation and action within somatomotor regions of the striatum. Abnormally high precision of priors arises through the dysfunctional synaptic integration of cortical inputs. These priors for sensation and action are projected into primary sensory and motor areas, triggering premonitory sensations and tics, which in turn elicit prediction errors for unexpected feelings and movements. We propose experimental paradigms to validate this Bayesian account of tics. Our model integrates behavioural, neuroimaging, and computational approaches to provide mechanistic insight into the pathophysiological basis of Tourette syndrome.

Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging (fMRI) has been used to relate neuronal network activity to cognitive processing and behavior. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, Linear Non-Gaussian Acyclic Models, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area.

Estimation of Functional Connectivity Modulations During Task Engagement and Their Neurovascular Underpinnings Through Hemodynamic Reorganization Method

This study proposes an approach to understand the effect of task engagement through integrated analysis of modulations in functional networks and associated changes in their neurovascular underpinnings at every voxel. For this purpose, a novel approach that brings reorganization in acquired task-functional magnetic resonance imaging information based on hemodynamic characteristics of every task stimulus is proposed and validated. At first, modulations in functional networks of visual target detection task were estimated at every voxel through proposed methodology. It revealed task stimulus dependency in the modulation of default mode network (DMN). The DMN modulated as task negative network (TNN) during target stimulus. On the contrary, it was not entirely TNN during nontarget stimulus. The frontal-parietal and visual networks modulated as task positive network during both task stimuli. Further, modulations of neurovascular underpinnings associated with engagement of task were estimated by correlating the hemodynamically reorganized task blood oxygen level dependent information with simultaneously acquired electroencephalography frequency powers. It revealed the strong association of neurovascular underpinnings with their modulation of functional networks and the associated neuronal activity during task engagement. Finally, graph theoretical parameters such as local, global efficiency and clustering coefficient were also measured at the specific regions for validating the results of proposed method. Modulation observed in graph theory measures clearly validated the activation and deactivation of functional networks observed by the proposed method during task engagement. Thus, the voxel-wise estimation of task-related modulation of functional networks and associated neurovascular underpinnings through proposed technique provide better insights into neuronal mechanism involved during engagement in a task.

Generic dynamic causal modelling: An illustrative application to Parkinson's disease

We present a technical development in the dynamic causal modelling of electrophysiological responses that combines qualitatively different neural mass models within a single network. This affords the option to couple various cortical and subcortical nodes that differ in their form and dynamics. Moreover, it enables users to implement new neural mass models in a straightforward and standardized way. This generic framework hence supports flexibility and facilitates the exploration of increasingly plausible models. We illustrate this by coupling a basal ganglia-thalamus model to a (previously validated) cortical model developed specifically for motor cortex. The ensuing DCM is used to infer pathways that contribute to the suppression of beta oscillations induced by dopaminergic medication in patients with Parkinson's disease. Experimental recordings were obtained from deep brain stimulation electrodes (implanted in the subthalamic nucleus) and simultaneous magnetoencephalography. In line with previous studies, our results indicate a reduction of synaptic efficacy within the circuit between the subthalamic nucleus and external pallidum, as well as reduced efficacy in connections of the hyperdirect and indirect pathway leading to this circuit. This work forms the foundation for a range of modelling studies of the synaptic mechanisms (and pathophysiology) underlying event-related potentials and cross-spectral densities.

Network connectivity of motor control in the ageing brain

Older individuals typically display stronger regional brain activity than younger subjects during motor performance. However, knowledge regarding age-related changes of motor network interactions between brain regions remains scarce. We here investigated the impact of ageing on the interaction of cortical areas during movement selection and initiation using dynamic causal modelling (DCM). We found that age-related psychomotor slowing was accompanied by increases in both regional activity and effective connectivity, especially for ‘core’ motor coupling targeting primary motor cortex (M1). Interestingly, younger participants within the older group showed strongest connectivity targeting M1, which steadily decreased with advancing age. Conversely, prefrontal influences on the motor system increased with advancing age, and were inversely correlated with reduced parietal influences and core motor coupling. Interestingly, higher net coupling within the prefrontal-premotor-M1 axis predicted faster psychomotor speed in ageing. Hence, as opposed to a uniform age-related decline, our findings are compatible with the idea of different age-related compensatory mechanisms, with an important role of the prefrontal cortex compensating for reduced coupling within the core motor network.

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Enhanced motor network activity and connectivity in ageing

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Parietal-premotor and premotor-M1 coupling decreases with advancing age.

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Prefrontal influences on the motor system increase with advancing age.

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Prefrontal cortex compensates for age-related decline in other motor connections.

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Prefrontal-premotor-M1 coupling predicts psychomotor speed in ageing.

Generative models for clinical applications in computational psychiatry

Despite the success of modern neuroimaging techniques in furthering our understanding of cognitive and pathophysiological processes, translation of these advances into clinically relevant tools has been virtually absent until now. Neuromodeling represents a powerful framework for overcoming this translational deadlock, and the development of computational models to solve clinical problems has become a major scientific goal over the last decade, as reflected by the emergence of clinically oriented neuromodeling fields like Computational Psychiatry, Computational Neurology, and Computational Psychosomatics. Generative models of brain physiology and connectivity in the human brain play a key role in this endeavor, striving for computational assays that can be applied to neuroimaging data from individual patients for differential diagnosis and treatment prediction. In this review, we focus on dynamic causal modeling (DCM) and its use for Computational Psychiatry. DCM is a widely used generative modeling framework for functional magnetic resonance imaging (fMRI) and magneto-/electroencephalography (M/EEG) data. This article reviews the basic concepts of DCM, revisits examples where it has proven valuable for addressing clinically relevant questions, and critically discusses methodological challenges and recent methodological advances. We conclude this review with a more general discussion of the promises and pitfalls of generative models in Computational Psychiatry and highlight the path that lies ahead of us. This article is categorized under: Neuroscience > Computation Neuroscience > Clinical Neuroscience.

Dynamic Causal Modeling of Preclinical Autosomal-Dominant Alzheimer's Disease

Dynamic causal modeling (DCM) is a framework for making inferences about changes in brain connectivity using neuroimaging data. We fitted DCMs to high-density EEG data from subjects performing a semantic picture matching task. The subjects are carriers of the PSEN1 mutation, which leads to early onset Alzheimer's disease, but at the time of EEG acquisition in 1999, these subjects were cognitively unimpaired. We asked 1) what is the optimal model architecture for explaining the event-related potentials in this population, 2) which connections are different between this Presymptomatic Carrier (PreC) group and a Non-Carrier (NonC) group performing the same task, and 3) which network connections are predictive of subsequent Mini-Mental State Exam (MMSE) trajectories. We found 1) a model with hierarchical rather than lateral connections between hemispheres to be optimal, 2) that a pathway from right inferotemporal cortex (IT) to left medial temporal lobe (MTL) was preferentially activated by incongruent items for subjects in the PreC group but not the NonC group, and 3) that increased effective connectivity among left MTL, right IT, and right MTL was predictive of subsequent MMSE scores.

Structural connectivity differences in motor network between tremor-dominant and nontremor Parkinson's disease

Motor phenotypes of Parkinson's disease (PD) are recognized to have different prognosis and therapeutic response, but the neural basis for this clinical heterogeneity remains largely unknown. The main aim of this study was to compare differences in structural connectivity metrics of the main motor network between tremor-dominant and nontremor PD phenotypes (TD-PD and NT-PD, respectively) using probabilistic tractography-based network analysis. A total of 63 PD patients (35 TD-PD patients and 28 NT-PD patients) and 30 healthy controls underwent a 3 T MRI. Next, probabilistic tractography-based network analysis was performed to assess structural connectivity in cerebello-thalamo-basal ganglia-cortical circuits, by measuring the connectivity indices of each tract and the efficiency of each node. Furthermore, dopamine transporter single-photon emission computed tomography (DAT-SPECT) with ¹²³ I-ioflupane was used to assess dopaminergic striatal depletion in all PD patients. Both PD phenotypes showed nodal abnormalities in the substantia nigra, in agreement with DAT-SPECT evaluation. In addition, NT-PD patients displayed connectivity alterations in nigro-pallidal and fronto-striatal pathways, compared with both controls and TD-PD patients, in which the same motor connections seemed to be relatively spared. Of note, in NT-PD group, rigidity-bradykinesia score correlated with fronto-striatal connectivity abnormalities. These findings demonstrate that structural connectivity alterations occur in the cortico-basal ganglia circuit of NT-PD patients, but not in TD-PD patients, suggesting that these anatomical differences may underlie different motor phenotypes of PD. Hum Brain Mapp 38:4716-4729, 2017. © 2017 Wiley Periodicals, Inc.

Dopamine controls Parkinson's tremor by inhibiting the cerebellar thalamus

Parkinson's resting tremor is related to altered cerebral activity in the basal ganglia and the cerebello-thalamo-cortical circuit. Although Parkinson's disease is characterized by dopamine depletion in the basal ganglia, the dopaminergic basis of resting tremor remains unclear: dopaminergic medication reduces tremor in some patients, but many patients have a dopamine-resistant tremor. Using pharmacological functional magnetic resonance imaging, we test how a dopaminergic intervention influences the cerebral circuit involved in Parkinson's tremor. From a sample of 40 patients with Parkinson's disease, we selected 15 patients with a clearly tremor-dominant phenotype. We compared tremor-related activity and effective connectivity (using combined electromyography-functional magnetic resonance imaging) on two occasions: ON and OFF dopaminergic medication. Building on a recently developed cerebral model of Parkinson's tremor, we tested the effect of dopamine on cerebral activity associated with the onset of tremor episodes (in the basal ganglia) and with tremor amplitude (in the cerebello-thalamo-cortical circuit). Dopaminergic medication reduced clinical resting tremor scores (mean 28%, range -12 to 68%). Furthermore, dopaminergic medication reduced tremor onset-related activity in the globus pallidus and tremor amplitude-related activity in the thalamic ventral intermediate nucleus. Network analyses using dynamic causal modelling showed that dopamine directly increased self-inhibition of the ventral intermediate nucleus, rather than indirectly influencing the cerebello-thalamo-cortical circuit through the basal ganglia. Crucially, the magnitude of thalamic self-inhibition predicted the clinical dopamine response of tremor. Dopamine reduces resting tremor by potentiating inhibitory mechanisms in a cerebellar nucleus of the thalamus (ventral intermediate nucleus). This suggests that altered dopaminergic projections to the cerebello-thalamo-cortical circuit have a role in Parkinson's tremor.aww331media15307619934001.

Testing Proposed Neuronal Models of Effective Connectivity Within the Cortico-basal Ganglia-thalamo-cortical Loop During Loss of Consciousness

In recent years, a number of brain regions and connectivity patterns have been proposed to be crucial for loss and recovery of consciousness but have not been compared in detail. In a 3 T resting-state functional magnetic resonance imaging paradigm, we test the plausibility of these different neuronal models derived from theoretical and empirical knowledge. Specifically, we assess the fit of each model to the dynamic change in effective connectivity between specific cortical and subcortical regions at different consecutive levels of propofol-induced sedation by employing spectral dynamic causal modeling. Surprisingly, our findings indicate that proposed models of impaired consciousness do not fit the observed patterns of effective connectivity. Rather, the data show that loss of consciousness, at least in the context of propofol-induced sedation, is marked by a breakdown of corticopetal projections from the globus pallidus. Effective connectivity between the globus pallidus and the ventral posterior cingulate cortex, present during wakefulness, fades in the transition from lightly sedated to full loss of consciousness and returns gradually as consciousness recovers, thereby, demonstrating the dynamic shift in brain architecture of the posterior cingulate "hub" during changing states of consciousness. These findings highlight the functional role of a previously underappreciated direct pallido-cortical connectivity in supporting consciousness.

Influence of extrinsic inputs and synaptic gains on dynamics of Wendling's neural mass model: A bifurcation analysis

We analyze the neurodynamics attributed by a model proposed by Wendling and co-workers (2002) [Wendling, F., Bartolomei, F., Bellanger, J.J. & Chauvel, P. (2002) Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition. Eur. J. Neurosci., 15, 1499.] to explain several different types of electroencephalographic activities. We could find three principal types of steady states when the system parameters change slowly: (i) the model produce a constant output when it is under a state of stable equilibrium point with a constant input. If a small perturbation is introduced (e.g., noisy input), the output changes into noise without oscillatory components, which is related to the normal background activity or low-voltage rapid activity, (ii) Hopf bifurcations lead to stable limit cycles, which we call Hopf cycles. The model generates a rhythmic oscillating output when it is under a state of Hopf cycles, which is related to slow rhythmic activity or slow quasi-sinusoidal activity, (iii) global bifurcations lead to homoclinic limit cycles that appear suddenly at high amplitude, which we call spike cycles. In general, the spike cycles are not harmonic but they have a spike-like appearance (anharmonic oscillation). The model produces a spike-like output when it is under a state of spike cycles, which is related to the sustained discharge of spikes. Finally, the bifurcation analysis demonstrates the influence of the interaction between the excitatory and inhibitory synaptic gains on the dynamics.

Parkinson's disease as a system-level disorder

Traditionally, the basal ganglia have been considered the main brain region implicated in Parkinson’s disease. This single area perspective gives a restricted clinical picture and limits therapeutic approaches because it ignores the influence of altered interactions between the basal ganglia and other cerebral components on Parkinsonian symptoms. In particular, the basal ganglia work closely in concert with cortex and cerebellum to support motor and cognitive functions. This article proposes a theoretical framework for understanding Parkinson’s disease as caused by the dysfunction of the entire basal ganglia–cortex–cerebellum system rather than by the basal ganglia in isolation. In particular, building on recent evidence, we propose that the three key symptoms of tremor, freezing, and impairments in action sequencing may be explained by considering partially overlapping neural circuits including basal ganglia, cortical and cerebellar areas. Studying the involvement of this system in Parkinson’s disease is a crucial step for devising innovative therapeutic approaches targeting it rather than only the basal ganglia. Possible future therapies based on this different view of the disease are discussed.

The study of brain functional connectivity in Parkinson's disease

Parkinson’s disease (PD) is a neurodegenerative disorder primarily affecting the aging population. The neurophysiological mechanisms underlying parkinsonian symptoms remain unclear. PD affects extensive neural networks and a more thorough understanding of network disruption will help bridge the gap between known pathological changes and observed clinical presentations in PD. Development of neuroimaging techniques, especially functional magnetic resonance imaging, allows for detection of the functional connectivity of neural networks in patients with PD. This review aims to provide an overview of current research involving functional network disruption in PD relating to motor and non-motor symptoms. Investigations into functional network connectivity will further our understanding of the mechanisms underlying the effectiveness of clinical interventions, such as levodopa and deep brain stimulation treatment. In addition, identification of PD-specific neural network patterns has the potential to aid in the development of a definitive diagnosis of PD.

Abnormal premotor-motor interaction in heterozygous Parkin- and Pink1 mutation carriers

OBJECTIVES

Mutations in the Parkin and PINK1 gene account for the majority of autosomal recessive early-onset Parkinson cases. There is increasing evidence that clinically asymptomatic subjects with single heterozygous mutations have a latent nigrostriatal dopaminergic deficit and could be taken as in vivo model of pre-symptomatic phase of Parkinsonism.

METHODS

We charted premotor-motor excitability changes as compensatory mechanisms for subcortical dopamine depletions using transcranial magnetic stimulation by applying magnetic resonance-navigated premotor-motor cortex conditioning in 15 asymptomatic, heterozygous Parkin and PINK1 mutation carriers (2 female; mean age 53±8years) and 16 age- and sex-matched controls (5 female; mean age 57±9years). Participants were examined at baseline and after acute l-dopa challenge.

RESULTS

There were l-dopa and group specific effects during premotor-motor conditioning at an interstimulus interval of 6ms indicating a normalisation of premotor-motor interactions in heterozygous Parkin and PINK1 mutation carriers after l-dopa intake. Non-physiologically high conditioned MEP amplitudes at this interval in mutation carriers decreased after l-dopa intake but increased in controls.

CONCLUSION

Premotor-motor excitability changes are part of the cortical reorganization in asymptomatic heterozygous Parkin- and PINK1 mutation carriers.

SIGNIFICANCE

These subjects offer opportunities to delineate motor network adaptation in pre-symptomatic Parkinsonism.

Atomoxetine restores the response inhibition network in Parkinson's disease

Parkinson's disease impairs the inhibition of responses, and whilst impulsivity is mild for some patients, severe impulse control disorders affect ∼10% of cases. Based on preclinical models we proposed that noradrenergic denervation contributes to the impairment of response inhibition, via changes in the prefrontal cortex and its subcortical connections. Previous work in Parkinson's disease found that the selective noradrenaline reuptake inhibitor atomoxetine could improve response inhibition, gambling decisions and reflection impulsivity. Here we tested the hypotheses that atomoxetine can restore functional brain networks for response inhibition in Parkinson's disease, and that both structural and functional connectivity determine the behavioural effect. In a randomized, double-blind placebo-controlled crossover study, 19 patients with mild-to-moderate idiopathic Parkinson's disease underwent functional magnetic resonance imaging during a stop-signal task, while on their usual dopaminergic therapy. Patients received 40 mg atomoxetine or placebo, orally. This regimen anticipates that noradrenergic therapies for behavioural symptoms would be adjunctive to, not a replacement for, dopaminergic therapy. Twenty matched control participants provided normative data. Arterial spin labelling identified no significant changes in regional perfusion. We assessed functional interactions between key frontal and subcortical brain areas for response inhibition, by comparing 20 dynamic causal models of the response inhibition network, inverted to the functional magnetic resonance imaging data and compared using random effects model selection. We found that the normal interaction between pre-supplementary motor cortex and the inferior frontal gyrus was absent in Parkinson's disease patients on placebo (despite dopaminergic therapy), but this connection was restored by atomoxetine. The behavioural change in response inhibition (improvement indicated by reduced stop-signal reaction time) following atomoxetine correlated with structural connectivity as measured by the fractional anisotropy in the white matter underlying the inferior frontal gyrus. Using multiple regression models, we examined the factors that influenced the individual differences in the response to atomoxetine: the reduction in stop-signal reaction time correlated with structural connectivity and baseline performance, while disease severity and drug plasma level predicted the change in fronto-striatal effective connectivity following atomoxetine. These results suggest that (i) atomoxetine increases sensitivity of the inferior frontal gyrus to afferent inputs from the pre-supplementary motor cortex; (ii) atomoxetine can enhance downstream modulation of frontal-subcortical connections for response inhibition; and (iii) the behavioural consequences of treatment are dependent on fronto-striatal structural connections. The individual differences in behavioural responses to atomoxetine highlight the need for patient stratification in future clinical trials of noradrenergic therapies for Parkinson's disease.

Atomoxetine Enhances Connectivity of Prefrontal Networks in Parkinson's Disease

Cognitive impairment is common in Parkinson's disease (PD), but often not improved by dopaminergic treatment. New treatment strategies targeting other neurotransmitter deficits are therefore of growing interest. Imaging the brain at rest ('task-free') provides the opportunity to examine the impact of a candidate drug on many of the brain networks that underpin cognition, while minimizing task-related performance confounds. We test this approach using atomoxetine, a selective noradrenaline reuptake inhibitor that modulates the prefrontal cortical activity and can facilitate some executive functions and response inhibition. Thirty-three patients with idiopathic PD underwent task-free fMRI. Patients were scanned twice in a double-blind, placebo-controlled crossover design, following either placebo or 40-mg oral atomoxetine. Seventy-six controls were scanned once without medication to provide normative data. Seed-based correlation analyses were used to measure changes in functional connectivity, with the right inferior frontal gyrus (IFG) a critical region for executive function. Patients on placebo had reduced connectivity relative to controls from right IFG to dorsal anterior cingulate cortex and to left IFG and dorsolateral prefrontal cortex. Atomoxetine increased connectivity from the right IFG to the dorsal anterior cingulate. In addition, the atomoxetine-induced change in connectivity from right IFG to dorsolateral prefrontal cortex was proportional to the change in verbal fluency, a simple index of executive function. The results support the hypothesis that atomoxetine may restore prefrontal networks related to executive functions. We suggest that task-free imaging can support translational pharmacological studies of new drug therapies and provide evidence for engagement of the relevant neurocognitive systems.

Computational neuroimaging strategies for single patient predictions

Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches - Bayesian model selection and generative embedding - which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning.

Reduced task-related functional connectivity during a set-shifting task in unmedicated early-stage Parkinson's disease patients

Background

Patients with Parkinson’s disease (PD) often suffer from cognitive impairments, including set-shifting deficits, in addition to the characteristic motor symptoms. It is hypothesized that the striatal dopamine depletion leads to a sub-optimal functional connectivity between task-related brain areas and consequently results in impaired task-performance. In this study, we aimed to examine this hypothesis by investigating the task-related functional connectivity of brain areas that are believed to be involved in set-shifting, such as the dorsolateral prefrontal cortex (DLPFC), posterior parietal cortex (PPC) and the superior frontal gyrus (SFG), during a set-shifting task. We obtained functional imaging data from 18 early-stage PD patients and 35 healthy controls, matched at the group level, using a newly developed rule-based set-shifting task that required participants to manually respond to arrow stimuli based on their location on the screen of their direction.

Results

We found that early stage PD patients, compared with controls, showed (1) a decrease in positive coupling between the left DLPFC and the right insular cortex, and the right SFG and anterior cingulate cortex, (2) an increase in negative coupling between the right SFG and the anterior cingulate cortex, primary motor cortex, precuneus, and PPC, and (3) an increase in negative coupling between the left DLPFC and the left and right SFG. These results indicate that important task-related areas of PD patients have decreased functional connectivity with task-related regions and increased connectivity with task-unrelated areas.

Conclusions

The disruption of functional connectivity in early stage PD patients during set-shifting reported here is likely compensated for by the local hyperactivation we reported earlier, thereby forestalling behavioural deficits.

Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography

We propose that sensory inputs are processed in terms of optimised predictions and prediction error signals within hierarchical neurocognitive models. The combination of non-invasive brain imaging and generative network models has provided support for hierarchical frontotemporal interactions in oddball tasks, including recent identification of a temporal expectancy signal acting on prefrontal cortex. However, these studies are limited by the need to invert magnetoencephalographic or electroencephalographic sensor signals to localise activity from cortical 'nodes' in the network, or to infer neural responses from indirect measures such as the fMRI BOLD signal. To overcome this limitation, we examined frontotemporal interactions estimated from direct cortical recordings from two human participants with cortical electrode grids (electrocorticography - ECoG). Their frontotemporal network dynamics were compared to those identified by magnetoencephalography (MEG) in forty healthy adults. All participants performed the same auditory oddball task with standard tones interspersed with five deviant tone types. We normalised post-operative electrode locations to standardised anatomic space, to compare across modalities, and inverted the MEG to cortical sources using the estimated lead field from subject-specific head models. A mismatch negativity signal in frontal and temporal cortex was identified in all subjects. Generative models of the electrocorticographic and magnetoencephalographic data were separately compared using the free-energy estimate of the model evidence. Model comparison confirmed the same critical features of hierarchical frontotemporal networks in each patient as in the group-wise MEG analysis. These features included bilateral, feedforward and feedback frontotemporal modulated connectivity, in addition to an asymmetric expectancy driving input on left frontal cortex. The invasive ECoG provides an important step in construct validation of the use of neural generative models of MEG, which in turn enables generalisation to larger populations. Together, they give convergent evidence for the hierarchical interactions in frontotemporal networks for expectation and processing of sensory inputs.

Reconfiguration of striatal connectivity for timing and action

The medial cortico-striatal-thalamo-cortical (CSTC) motor circuit is a core system that exerts control over interval timing and action. A common network generates these behaviors possibly owing to cellular coding of temporal and non-temporal information, which in turn promotes reconfiguration of functional connectivity in accord with behavioral goals. At the neuroanatomical level, support for flexible CSTC reconfiguration comes from studies of temporal illusions demonstrating that this system calibrates the experience of time through functional interactions with various context-sensitive brain regions. Revelations that CSTC effective connectivity is pivotal for context-dependent facets of voluntary actions, namely action planning, complement its role in predictive processes such as timing. These observations suggest that the CSTC is positioned to represent high-level information about 'what to do' and 'when to do it' by dynamically reconfiguring effective connectivity as circumstances arise.

Test-retest reliability of effective connectivity in the face perception network

Computational approaches have great potential for moving neuroscience toward mechanistic models of the functional integration among brain regions. Dynamic causal modeling (DCM) offers a promising framework for inferring the effective connectivity among brain regions and thus unraveling the neural mechanisms of both normal cognitive function and psychiatric disorders. While the benefit of such approaches depends heavily on their reliability, systematic analyses of the within-subject stability are rare. Here, we present a thorough investigation of the test-retest reliability of an fMRI paradigm for DCM analysis dedicated to unraveling intra- and interhemispheric integration among the core regions of the face perception network. First, we examined the reliability of face-specific BOLD activity in 25 healthy volunteers, who performed a face perception paradigm in two separate sessions. We found good to excellent reliability of BOLD activity within the DCM-relevant regions. Second, we assessed the stability of effective connectivity among these regions by analyzing the reliability of Bayesian model selection and model parameter estimation in DCM. Reliability was excellent for the negative free energy and good for model parameter estimation, when restricting the analysis to parameters with substantial effect sizes. Third, even when the experiment was shortened, reliability of BOLD activity and DCM results dropped only slightly as a function of the length of the experiment. This suggests that the face perception paradigm presented here provides reliable estimates for both conventional activation and effective connectivity measures. We conclude this paper with an outlook on potential clinical applications of the paradigm for studying psychiatric disorders. Hum Brain Mapp 37:730-744, 2016. © 2015 Wiley Periodicals, Inc.