A multi-scale cortical wiring space links cellular architecture and functional dynamics in the human brain.

The vast net of fibres within and underneath the cortex is optimised to support the convergence of different levels of brain organisation. Here, we propose a novel coordinate system of the human cortex based on an advanced model of its connectivity. Our approach is inspired by seminal, but so far largely neglected models of cortico-cortical wiring established by postmortem anatomical studies and capitalises on cutting-edge in vivo neuroimaging and machine learning. The new model expands the currently prevailing diffusion magnetic resonance imaging (MRI) tractography approach by incorporation of additional features of cortical microstructure and cortico-cortical proximity. Studying several datasets and different parcellation schemes, we could show that our coordinate system robustly recapitulates established sensory-limbic and anterior-posterior dimensions of brain organisation. A series of validation experiments showed that the new wiring space reflects cortical microcircuit features (including pyramidal neuron depth and glial expression) and allowed for competitive simulations of functional connectivity and dynamics based on resting-state functional magnetic resonance imaging (rs-fMRI) and human intracranial electroencephalography (EEG) coherence. Our results advance our understanding of how cell-specific neurobiological gradients produce a hierarchical cortical wiring scheme that is concordant with increasing functional sophistication of human brain organisation. Our evaluations demonstrate the cortical wiring space bridges across scales of neural organisation and can be easily translated to single individuals

Grey matter abnormalities in methcathinone abusers with a Parkinsonian syndrome

Funding Information: The study was supported by Grants GARNR9199 and GARLA0148P of the Estonian Science Foundation, and Grant No. 5.8.2 of the National Research Program of Latvia. Ricarda A L Menke is employed by the University of Oxford and her salary is funded by the Medical Research Council of the UK. Heidi Johansen-Berg is employed by the Universities of Oxford and Oslo, holds grants from the Wellcome Trust, National Institutes of Health Research, Education Endowment Foundation, Stroke Association, and Royalties from Elsevier. Charlotte J Stagg holds a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society. Ain?rs Stepens holds Grant No. 5.8.2 of the National Research Program of Latvia, which supported this study. Pille Taba holds Grant 9199 of the Estonian Science Foundation, which supported this study, is principal investigator of Grant 3.2.1001.11-0017 of the EU European Regional Development Fund, and participates in Grant IUT2-4 of the Estonian Research Council. Publisher Copyright: © 2016 The Authors. Brain and Behavior published by Wiley Periodicals, Inc. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.Background: A permanent Parkinsonian syndrome occurs in intravenous abusers of the designer psychostimulant methcathinone (ephedrone). It is attributed to deposition of contaminant manganese, as reflected by characteristic globus pallidus hyperintensity on T1-weighted MRI. Methods: We have investigated brain structure and function in methcathinone abusers (n = 12) compared to matched control subjects (n = 12) using T1-weighted structural and resting-state functional MRI. Results: Segmentation analysis revealed significant (p <.05) subcortical grey matter atrophy in methcathinone abusers within putamen and thalamus bilaterally, and the left caudate nucleus. The volume of the caudate nuclei correlated inversely with duration of methcathinone abuse. Voxel-based morphometry showed patients to have significant grey matter loss (p <.05) bilaterally in the putamina and caudate nucleus. Surface-based analysis demonstrated nine clusters of cerebral cortical thinning in methcathinone abusers, with relative sparing of prefrontal, parieto-occipital, and temporal regions. Resting-state functional MRI analysis showed increased functional connectivity within the motor network of patients (p <.05), particularly within the right primary motor cortex. Conclusion: Taken together, these results suggest that the manganese exposure associated with prolonged methcathinone abuse results in widespread structural and functional changes affecting both subcortical and cortical grey matter and their connections. Underlying the distinctive movement disorder caused by methcathinone abuse, there is a more widespread pattern of brain involvement than is evident from the hyperintensity restricted to the basal ganglia as shown by T1-weighted structural MRI.Peer reviewe

Dynamic activity patterns and cortico-subcortical interactions in the human brain

openQuesto progetto di tesi si propone di indagare la natura della connettività funzionale dinamica (dFC), con un particolare focus sulle sue basi neurali e sulla sua possibile rilevanza comportamentale. Nello specifico, partendo da uno studio pubblicato nel 2022 da Favaretto e colleghi, valuteremo il ruolo delle comunicazioni cortico-sottocorticali nella modellazione della dFC, nonché l'importanza dei suoi parametri nel predire specifici aspetti demografici e comportamentali.This thesis project aims at broadly investigating the nature of dynamic funtctional connectivity (dFC), with a specific focus on its neural underpinnings and potential behavioral relevance. In particular, leading from a study published in 2022 by Favaretto and collegues, we will assess the role of cortico-subcortical communications in shaping dFC as well as the influence of dFC metrics on demographic traits and individual behavior

Nonlinear ICA of fMRI reveals primitive temporal structures linked to rest, task, and behavioral traits

Accumulating evidence from whole brain functional magnetic resonance imaging (fMRI) suggests that the human brain at rest is functionally organized in a spatially and temporally constrained manner. However, because of their complexity, the fundamental mechanisms underlying time-varying functional networks are still not well under-stood. Here, we develop a novel nonlinear feature extraction framework called local space-contrastive learning (LSCL), which extracts distinctive nonlinear temporal structure hidden in time series, by training a deep temporal convolutional neural network in an unsupervised, data-driven manner. We demonstrate that LSCL identifies certain distinctive local temporal structures, referred to as temporal primitives, which repeatedly appear at different time points and spatial locations, reflecting dynamic resting-state networks. We also show that these temporal primitives are also present in task-evoked spatiotemporal responses. We further show that the temporal primitives capture unique aspects of behavioral traits such as fluid intelligence and working memory. These re-sults highlight the importance of capturing transient spatiotemporal dynamics within fMRI data and suggest that such temporal primitives may capture fundamental information underlying both spontaneous and task-induced fMRI dynamics.Peer reviewe

Rat Model of Pre-Motor Parkinson\u27s Disease: Behavioral and MRI Characterization.

Background: Parkinson\u27s disease (PD) is a chronic, progressive, neurodegenerative disorder with currently no known cure. PD has a significant impact on quality of life of the patients, as well as, the caregivers and family members. It is the second most common cause of chronic neurological disability in US and Europe. According to National Parkinson\u27s Foundation, there are almost 1 million patients in the Unites States and 50,000 to 60,000 new cases of PD are diagnosed each year. The total number of cases of PD is predicted to double by 2030. The annual cost associated with this disease is estimated to be $10.8 billion in the United States, including the cost of treatment and the cost of the disability. Although it is primarily thought of as a movement-disorder and is clinically diagnosed based on motor symptoms, non-motor symptoms such as cognitive and emotional deficits are thought to precede the clinical diagnosis by almost 20 years. By the time of clinical diagnosis, there is 80% loss in the dopamine content in the striatum and 50% degeneration of the substantia nigra dopamine cells. The research presented in this thesis was an attempt to develop an animal model of PD in its pre-motor stages. Such a model would allow us to develop pre-clinical markers for PD, and facilitate the development and testing of potential treatment strategies for the non-motor symptoms of the disorder. Specific Aims: There were five specific aims for this research: * The first specific aim dealt with development of a rat model of PD with slow, progressive onset of motor deficits, determination of timeline for future studies, and quantification the dopamine depletion in this model at a pre-motor stage. * The second and the third specific aims focused on testing for emotional (aversion) deficits and cognitive (executive functioning) deficits in this rat model at the 3 week timepoint determined during specific aim 1. * The fourth specific aim was to determine the brain network changes associated with the behavioral changes observed our rat model using resting state connectivity as a measure. * The fifth and the final specific aim was to test sodium butyrate, a drug from the histone deacetylase inhibitor family, as a potential treatment option for cognitive deficits in PD. Results: The 6-hydroxy dopamine based stepwise striatal lesion model of pre-motor PD, developed during this research, exhibits delayed onset of Parkinsonian gait like symptoms by week 4 after the lesions. At 3 weeks post lesion (3WKPD), the rats exhibit 27% reduction in striatal dopamine and 23%reduction in substantia nigra dopamine cells, with lack of any apparent motor deficits. The 3WKPD rats also exhibited changes in aversion. The fMRI study with the aversive scent pointed towards possible amygdala dysfunction sub-serving the aversion deficits. The executive function deficits tested using a rat analog of the Wisconsin card sorting test, divulged an extra-dimensional set shifting deficit in the 3WKPD rats similar to those reported in PD patients. The resting state connectivity study indicated significant changes in the 3WKPD rats compared to age matched controls. We observed increased overall connectivity of the motor cortex and increased CPu connectivity with prefrontal cortex, cingulate cortex, and hypothalamus in the 3WKPD rats compared to the controls. These observations parallel the observations in unmedicated early-stage PD patients. We also observed negative correlation between amygdala and prefrontal cortex as reported in humans. This negative correlation was lost in 3WKPD rats. Sodium butyrate treatment, tested in the cognitive deficit study, was able to ameliorate the extra-dimensional set shifting deficit observed in this model. This treatment also improved the attentional set formation. Conclusion: Taken together, our observations indicate that, the model of pre-motor stage PD developed during this research is a very high face validity rat model of late Braak stage 2 or early Braak stage 3 PD. Sodium butyrate was able to alleviate the cognitive deficits observed in our rat model. Hence, along with the prior reports of anti-depressant and neuroprotective effects of this drug, our results point towards a possible treatment strategy for the non-motor deficits of PD

Deep Interpretability Methods for Neuroimaging

Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics. In contrast, introspection of discriminatively trained deep learning models may uncover disorder-relevant elements of the signal at the level of individual time points and spatial locations. Nevertheless, the difficulty of reliable training on high-dimensional but small-sample datasets and the unclear relevance of the resulting predictive markers prevent the widespread use of deep learning in functional neuroimaging. In this dissertation, we address these challenges by proposing a deep learning framework to learn from high-dimensional dynamical data while maintaining stable, ecologically valid interpretations. The developed model is pre-trainable and alleviates the need to collect an enormous amount of neuroimaging samples to achieve optimal training. We also provide a quantitative validation module, Retain and Retrain (RAR), that can objectively verify the higher predictability of the dynamics learned by the model. Results successfully demonstrate that the proposed framework enables learning the fMRI dynamics directly from small data and capturing compact, stable interpretations of features predictive of function and dysfunction. We also comprehensively reviewed deep interpretability literature in the neuroimaging domain. Our analysis reveals the ongoing trend of interpretability practices in neuroimaging studies and identifies the gaps that should be addressed for effective human-machine collaboration in this domain. This dissertation also proposed a post hoc interpretability method, Geometrically Guided Integrated Gradients (GGIG), that leverages geometric properties of the functional space as learned by a deep learning model. With extensive experiments and quantitative validation on MNIST and ImageNet datasets, we demonstrate that GGIG outperforms integrated gradients (IG), which is considered to be a popular interpretability method in the literature. As GGIG is able to identify the contours of the discriminative regions in the input space, GGIG may be useful in various medical imaging tasks where fine-grained localization as an explanation is beneficial

The Human Connectome Project: A retrospective

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the WU-Minn-Ox HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The HCP-style neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium