Measuring cortical connectivity in Alzheimer's disease as a brain neural network pathology: Toward clinical applications

Objectives: The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of Alzheimer’s disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity underlying human cognition. Methods: We reviewed papers registered in PubMed and other scientific repositories on the use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to cognitively intact elderly individuals (Controls). Results: Hundreds of peer-reviewed (cross-sectional and longitudinal) papers have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic, and anterior–posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited. Conclusions: Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains in pre-clinical, prodromal, and dementia stages of AD. (JINS, 2016, 22, 138–163

Neural indicators of fatigue in chronic diseases : A systematic review of MRI studies

The authors would like to thank the Sir Jules Thorn Charitable Trust for their financial support.Peer reviewedPublisher PD

Spatiotemporal dynamics of single-letter reading: a combined ERP-FMRI study

This work investigates the neural correlates of single-letter reading by combining event-related potentials (ERPs) and functional magnetic resonance imaging (fMRI), thus exploiting their complementary spatiotemporal resolutions. Three externally-paced reading tasks were administered with an event-related design: passive observation of letters and symbols and active reading aloud of letters. ERP and fMRI data were separately recorded from 8 healthy adults during the same experimental conditions. Due to the presence of artifacts in the EEG signals, two subjects were discarded from further analysis. Independent Component Analysis was applied to ERPs, after dimensionality reduction by Principal Component Analysis: some independent components were clearly related to specific reading functions and the associated current density distributions in the brain were estimated with Low Resolution Electromagnetic Tomography Analysis method (LORETA). The impulse hemodynamic response function was modeled as a linear combination of linear B-spline functions and fMRI statistical analysis was performed by multiple linear regression. fMRI and LORETA maps were superimposed in order to identify the overlapping activations and the activated regions specifically revealed by each modality. The results showed the existence of neuronal networks functionally specific for letter processing and for explicit verbal-motor articulation, including the temporo-parietal and frontal regions. Overlap between fMRI and LORETA results was observed in the inferior temporal-middle occipital gyrus, suggesting that this area has a crucial and multifunctional role for linguistic and reading processes, likely because its spatial location and strong interconnection with the main visual and auditory sensory systems may have favored its specialization in grapheme-phoneme matching

Towards mapping neuro-behavioral heterogeneity of psychedelic neurobiology in humans

Precision psychiatry aims to identify markers of inter-individual variability that allow predicting the right treatment for each patient. However, bridging the gap between molecular-level manipulations and neural systems-level functional alterations remains an unsolved problem in psychiatry. After decades of low success rates in pharmaceutical R&D for psychiatric drugs, multiple studies now point to the potential of psychedelics as a promising fast-acting and long-lasting treatment for some psychiatric symptoms. Yet, given the highly psychoactive nature of these substances, a precision medicine approach is essential to map the neural signals related to clinical efficacy in order to identify patients who can maximally benefit from this treatment. Recent studies have shown that bridging the gap between pharmacology, systems-level neural response in humans and individual experience is possible for psychedelic substances, therefore paving the way for a precision neuropsychiatric therapeutic development. Specifically, it has been shown that the integration of brain-wide PET or transcriptomic data, i.e. receptor distribution for the serotonin 2A receptor, with computational neuroimaging methods can simulate the effect of psychedelics on the human brain. These novel 'computational psychiatry' approaches allow for modeling inter-individual differences in neural as well as subjective effects of psychedelic substances. Collectively, this review provides a deep dive into psychedelic pharmaco-neuroimaging studies with a core focus on how recent computational psychiatry advances in biophysically based circuit modeling can be leveraged to predict individual responses. Finally, we emphasize the importance of human pharmacological neuroimaging for the continued precision therapeutic development of psychedelics. Keywords: computational modeling; fMRI; neuroimaging; precision psychiatry; psychedelics; serotonin

Multimodal view on resting-state brain activity in Parkinson’s disease: examining the relation between functional resting-state networks and metabolic network activity

Research focusing on the pathophysiology of neurodegenerative disorders has undergone a fundamental shift towards a network perspective in the last decades. Besides regional aggregation of misfolded proteins and changes in cellular metabolism, accompanying changes of synaptic activity evolve and evoke dysregulation within neural circuits including remote brain regions. Modern theories of neurodegeneration propose a stereotypic pattern of these cerebral pathologies, which partly are in vivo accessible by multimodal neuroimaging techniques. The most often used indirect measurement of functional network integrity is resting-state functional magnetic resonance imaging, which depends on a complex interplay of hemodynamics, blood volume, and blood flow. Less is known about a potential metabolic component underlying resting-state networks in healthy brains and changes thereof in neurodegeneration and the influence of different transmitter systems. The current work therefore sought to investigate the association between functional resting-state networks and metabolic network activity and focused on metabolic consequences of nigrostriatal and striatocortical dysfunction in Parkinson’s disease. In the current work, a multimodal data set of the TP10 KFO219 cohort was analyzed regarding 1) the impact of nigrostriatal dopamine depletion on resting-state networks and 2) the relation between changes in functional connectivity and metabolic network activity. The first study addressed the subset of the KFO219 TP10 cohort who completed the trimodal imaging protocol (42 patients vs. 14 controls). Dopamine deficiency in Parkinson’s patients was examined by voxel-wise comparison of 6-[18F]fluoro-L-Dopa positron emission tomography scans. Resulting clusters served as seeds for restingstate functional connectivity maps that were compared between both groups by voxelwise t-tests. Metabolic activity was extracted from 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography scans for respective cortical clusters with striatocortical dysconnectivity and the relation to functional connectivity values was analyzed. In a separate study, functional and metabolic resting-state networks were obtained by performing spatial independent component analyses in a subset of the same cohort who underwent 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography and functional magnetic resonance imaging (56 vs 16) and completed neuropsychological testing. Multimodally obtained regions of interest in the default mode network were defined and metabolic activity as well as metabolic connectivity compared to functional connectivity differences between patients without or with mild cognitive impairment and healthy controls. Moreover, a third study was initiated in the context of the present work with the aim of establishing a dynamic 2-[18F]fluoro-2-deoxy-D-glucose positronemission tomography acquisition with a constant infusion protocol for examining interregional metabolic connectivity on single subject level and enable comparable analysis of hemodynamic and metabolic fluctuations in Parkinson’s disease. In the first study, a significant association between striatocortical functional connectivity changes of the data-driven defined dopamine depleted posterior putamen and metabolic activity of the cortical target area in the inferior parietal cortex was found in Parkinson’s disease. Interestingly, striatocortical connectivity of the inferior parietal cortex was associated with motor and cognitive impairment. In a second study, the multivariate approach revealed a moderate spatial convergence for the posterior default mode network in functional and metabolic data. For all multimodally obtained default mode network regions, a significant trend towards an increment of metabolic deficits from healthy controls via unimpaired patients to patients with mild cognitive impairment was identified. In addition, posterior default mode network regions with the strongest metabolic deficits and gradual decline in comparison to controls, also showed the strongest increases in both metabolic and functional connectivity compared to controls. The verification of the applicability of a constant infusion dynamic 2-[18F]fluoro-2-deoxy- D-glucose positron emission tomography protocol in Parkinson’s disease patients was started in a self-initiated study, which finished the acquisition phase with 10 participants per group by the time the current work was submitted. Together the first two studies highlight the added value of multimodal imaging in investigating human brain function and the pathophysiology of neurodegenerative disorders, in particular their great potential for identifying links between individual pathologies. The second study partly continued, and answered questions raised in response to the first study, which hinted at an involvement of default mode network regions in cognitive symptoms of Parkinson’s disease and a relation between functional network degeneration and metabolic activity. The current work shows exemplary the complementarity of both measures of brain network activity and their individual significance for cognitive symptoms in Parkinson’s disease. The presented work highlights how multimodal resting-state studies can provide new insights into the (patho-)physiological network organization of brain activity by confirming insights obtained by one modality and deepen our understanding of disease processes. The selfinitiated study further laid the ground for multimodal characterization of metabolic and hemodynamic network changes on single-subject level and the evaluation of dynamic positron emission tomography-based connectivity as metabolic network marker for Parkinson’s disease

Multimodal neuroimaging signatures of early cART-treated paediatric HIV - Distinguishing perinatally HIV-infected 7-year-old children from uninfected controls

Introduction: HIV-related brain alterations can be identified using neuroimaging modalities such as proton magnetic resonance spectroscopy (1H-MRS), structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and functional MRI (fMRI). However, few studies have combined multiple MRI measures/features to identify a multivariate neuroimaging signature that typifies HIV infection. Elastic net (EN) regularisation uses penalised regression to perform variable selection, shrinking the weighting of unimportant variables to zero. We chose to use the embedded feature selection of EN logistic regression to identify a set of neuroimaging features characteristic of paediatric HIV infection. We aimed to determine 1) the most useful features across MRI modalities to separate HIV+ children from HIV- controls and 2) whether better classification performance is obtained by combining multimodal MRI features rather than using features from a single modality. Methods: The study sample comprised 72 HIV+ 7-year-old children from the Children with HIV Early Antiretroviral Therapy (CHER) trial in Cape Town, who initiated combination antiretroviral therapy (cART) in infancy and had their viral loads suppressed from a young age, and 55 HIV- control children. Neuroimaging features were extracted to generate 7 MRI-derived sets. For sMRI, 42 regional brain volumes (1st set), mean cortical thickness and gyrification in 68 brain regions (2nd and 3rd set) were used. For DTI data: radial (RD), axial (AD), mean (MD) diffusivities, and fractional anisotropy (FA) in each of 20 atlas regions were extracted for a total of 80 DTI features (4th set). For 1H-MRS, concentrations of 14 metabolites and their ratios to creatine in the basal ganglia, peritrigonal white matter, and midfrontal gray matter voxels (5th, 6th and 7th set) were considered. A logistic EN regression model with repeated 10-fold cross validation (CV) was implemented in R, initially on each feature set separately. Sex, age and total intracranial volume (TIV) were included as confounders with no shrinkage penalty. For each model, the classification performance for HIV+ vs HIV- was assessed by computing accuracy, specificity, sensitivity, and mean area under the receiver operator characteristic curve (AUC) across 10 CV folds and 100 iterations. To combine feature sets, the best performing set was concatenated with each of the other sets and further EN regressions were run. The combination giving the largest AUC was combined with each of the remaining sets until there was no further increase in AUC. Two concatenation techniques were explored: nested and non-nested modelling. All models were assessed for their goodness of fit using χ 2 likelihood ratio tests for non-nested models and Akaike information criterion (AIC) for nested models. To identify features most useful in distinguishing HIV infection, the EN model was retrained on all the data, to find features with non-zero weights. Finally, multivariate imputation using chained equations (MICE) was explored to investigate the effect of increased sample size on classification and feature selection. Results: The best performing modality in the single modality analysis was sMRI volume