Search

In this paper, we propose a new representation of the cortical surface that may be used to study the cortex folding process and to recover some putative stable anatomical landmarks called sulcal roots usually buried in the depth of adult brains. This representation is a primal sketch derived from a scale space computed for the mean curvature of the cortical surface. This scale-space stems from a diffusion equation geodesic to the cortical surface. The primal sketch is made up of objects defined from mean curvature minima and saddle points. The resulting sketch aims first at highlighting significant elementary cortical folds, second at representing the fold merging process during brain growth. The relevance of the framework is illustrated by the study of central sulcus sulcal roots from antenatal to adult age. Some results are proposed for ten different brains. Some preliminary results are also provided for superior temporal sulcus. Index Terms--Morphometry, spatial normalization, sulcogenesis, variability.

Given the association of Epstein-Barr virus (EBV) with subjective perception of fatigue and demyelination in clinical conditions, the question about potential subclinical effects in the adult general population remains open. We investigate the association between individuals' EBV immune response and perceived fatigue in a community dwelling cohort (n = 864, age 62 ± 10 years old; 49% women) while monitoring brain tissue properties. Fatigue levels are assessed with the established fatigue severity scale, the EBNA-1 and VCA p18 immunoglobulin G (IgG) chronic response - with multiplex serology and the estimates of local brain volume, myelin content, and axonal density - using relaxometry- and multi-shell diffusion-based magnetic resonance imaging (MRI). In our analysis we adjust for the effects of demographic and cardiovascular risk factors, sleep apnea, depression, and polygenic risk score for multiple sclerosis . We demonstrate that EBNA-1 IgG levels are positively associated with perceived levels of fatigue, whilst VCA p18 IgG levels show a positive correlation with myelin content and a negative one with an estimate of axonal g-ratio in male participants. In the context of EBVs immune response, the polygenic risk for multiple sclerosis is not associated with increased fatigue levels, brain myelination or atrophy. Our findings bring empirical evidence about the potential role of EBVs chronic immune response in perceived fatigue and hint towards a protective role of myelination specific for men. They underscore the added value of advanced assessment of brain tissue microstructure in uncovering the mechanisms behind frequent fatigue complaints associated with EBV infection and multiple sclerosis., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Draganski reports financial support was provided by Swiss National Science Foundation. Draganski reports financial support was provided by European Commission. Fellay reports financial support was provided by Swiss National Science Foundation. Draganski reports equipment, drugs, or supplies was provided by Roger de Spoelberch Foundation. Fellay reports financial support was provided by Swiss State Secretariat for Education Research and Innovation. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Inc.)

Despite major advances, our understanding of the neurobiology of life course socioeconomic conditions is still scarce. This study aimed to provide insight into the pathways linking socioeconomic exposures-household income, last known occupational position, and life course socioeconomic trajectories-with brain microstructure and cognitive performance in middle to late adulthood. We assessed socioeconomic conditions alongside quantitative relaxometry and diffusion-weighted magnetic resonance imaging indicators of brain tissue microstructure and cognitive performance in a sample of community-dwelling men and women ( N  = 751, aged 50-91 years). We adjusted the applied regression analyses and structural equation models for the linear and nonlinear effects of age, sex, education, cardiovascular risk factors, and the presence of depression, anxiety, and substance use disorders. Individuals from lower-income households showed signs of advanced brain white matter (WM) aging with greater mean diffusivity (MD), lower neurite density, lower myelination, and lower iron content. The association between household income and MD was mediated by neurite density ( B  = 0.084, p  = 0.003) and myelination ( B  = 0.019, p  = 0.009); MD partially mediated the association between household income and cognitive performance ( B  = 0.017, p  < 0.05). Household income moderated the relation between WM microstructure and cognitive performance, such that greater MD, lower myelination, or lower neurite density was only associated with poorer cognitive performance among individuals from lower-income households. Individuals from higher-income households showed preserved cognitive performance even with greater MD, lower myelination, or lower neurite density. These findings provide novel mechanistic insights into the associations between socioeconomic conditions, brain anatomy, and cognitive performance in middle to late adulthood., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 the authors.)

Introduction: In this study, we applied multivariate methods to identify brain regions that have a critical role in shaping the connectivity patterns of networks associated with major psychiatric diagnoses, including schizophrenia (SCH), major depressive disorder (MDD) and bipolar disorder (BD) and healthy controls (HC). We used T1w images from 164 subjects: Schizophrenia ( n  = 17), bipolar disorder ( n  = 25), major depressive disorder ( n  = 68) and a healthy control group ( n  = 54)., Methods: We extracted regions of interest (ROIs) using a method based on the SHOOT algorithm of the SPM12 toolbox. We then performed multivariate structural covariance between the groups. For the regions identified as significant in t term of their covariance value, we calculated their eigencentrality as a measure of the influence of brain regions within the network. We applied a significance threshold of p = 0.001. Finally, we performed a cluster analysis to determine groups of regions that had similar eigencentrality profiles in different pairwise comparison networks in the observed groups., Results: As a result, we obtained 4 clusters with different brain regions that were diagnosis-specific. Cluster 1 showed the strongest discriminative values between SCH and HC and SCH and BD. Cluster 2 had the strongest discriminative value for the MDD patients, cluster 3 - for the BD patients. Cluster 4 seemed to contribute almost equally to the discrimination between the four groups., Discussion: Our results suggest that we can use the multivariate structural covariance method to identify specific regions that have higher predictive value for specific psychiatric diagnoses. In our research, we have identified brain signatures that suggest that degeneracy shapes brain networks in different ways both within and across major psychiatric disorders., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Paunova, Ramponi, Kandilarova, Todeva-Radneva, Latypova, Stoyanov and Kherif.)

Our knowledge of the mechanisms underlying the vulnerability of the brain's white matter microstructure to cardiovascular risk factors (CVRFs) is still limited. We used a quantitative magnetic resonance imaging (MRI) protocol in a single centre setting to investigate the cross-sectional association between CVRFs and brain tissue properties of white matter tracts in a large community-dwelling cohort (n = 1104, age range 46-87 years). Arterial hypertension was associated with lower myelin and axonal density MRI indices, paralleled by higher extracellular water content. Obesity showed similar associations, though with myelin difference only in male participants. Associations between CVRFs and white matter microstructure were observed predominantly in limbic and prefrontal tracts. Additional genetic, lifestyle and psychiatric factors did not modulate these results, but moderate-to-vigorous physical activity was linked to higher myelin content independently of CVRFs. Our findings complement previously described CVRF-related changes in brain water diffusion properties pointing towards myelin loss and neuroinflammation rather than neurodegeneration., (© 2023. The Author(s).)

There is an ongoing debate about differential clinical outcome and associated adverse effects of deep brain stimulation (DBS) in Parkinson's disease (PD) targeting the subthalamic nucleus (STN) or the globus pallidus pars interna (GPi). Given that functional connectivity profiles suggest beneficial DBS effects within a common network, the empirical evidence about the underlying anatomical circuitry is still scarce. Therefore, we investigate the STN and GPi-associated structural covariance brain patterns in PD patients and healthy controls. We estimate GPi's and STN's whole-brain structural covariance from magnetic resonance imaging (MRI) in a normative mid- to old-age community-dwelling cohort (n = 1184) across maps of grey matter volume, magnetization transfer (MT) saturation, longitudinal relaxation rate (R1), effective transversal relaxation rate (R2*) and effective proton density (PD*). We compare these with the structural covariance estimates in patients with idiopathic PD (n = 32) followed by validation using a reduced size controls' cohort (n = 32). In the normative data set, we observed overlapping spatially distributed cortical and subcortical covariance patterns across maps confined to basal ganglia, thalamus, motor, and premotor cortical areas. Only the subcortical and midline motor cortical areas were confirmed in the reduced size cohort. These findings contrasted with the absence of structural covariance with cortical areas in the PD cohort. We interpret with caution the differential covariance maps of overlapping STN and GPi networks in patients with PD and healthy controls as correlates of motor network disruption. Our study provides face validity to the proposed extension of the currently existing structural covariance methods based on morphometry features to multiparameter MRI sensitive to brain tissue microstructure., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Given controversial findings of reduced depressive symptom severity and increased hippocampus volume in CYP2C19 poor metabolizers, we sought to provide empirical evidence from a large-scale single-center longitudinal cohort in the community-dwelling adult population-Colaus|PsyCoLaus in Lausanne, Switzerland (n = 4152). We looked for CYP2C19 genotype-related behavioral and brain anatomy patterns using a comprehensive set of psychometry, water diffusion- and relaxometry-based magnetic resonance imaging (MRI) data (BrainLaus, n = 1187). Our statistical models tested for differential associations between poor metabolizer and other metabolizer status with imaging-derived indices of brain volume and tissue properties that explain individuals' current and lifetime mood characteristics. The observed association between CYP2C19 genotype and lifetime affective status showing higher functioning scores in poor metabolizers, was mainly driven by female participants (ß = 3.9, p = 0.010). There was no difference in total hippocampus volume between poor metabolizer and other metabolizer, though there was higher subiculum volume in the right hippocampus of poor metabolizers (ß = 0.03, p FDRcorrected  = 0.036). Our study supports the notion of association between mood phenotype and CYP2C19 genotype, however, finds no evidence for concomitant hippocampus volume differences, with the exception of the right subiculum., (© 2022. The Author(s).)

Socioeconomic status (SES) plays a significant role in health and disease. At the same time, early-life conditions affect neural function and structure, suggesting the brain may be a conduit for the biological embedding of SES. Here, we investigate the brain anatomy signatures of SES in a large-scale population cohort aged 45-85 years. We assess both gray matter morphometry and tissue properties indicative of myelin content. Higher life course SES is associated with increased volume in several brain regions, including postcentral and temporal gyri, cuneus, and cerebellum. We observe more widespread volume differences and higher myelin content in the sensorimotor network but lower myelin content in the temporal lobe associated with childhood SES. Crucially, childhood SES differences persisted in adult brains even after controlling for adult SES, highlighting the unique contribution of early-life conditions to brain anatomy, independent of later changes in SES. These findings inform on the biological underpinnings of social inequality, particularly as they pertain to early-life conditions., (© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Motion during the acquisition of magnetic resonance imaging (MRI) data degrades image quality, hindering our capacity to characterise disease in patient populations. Quality control procedures allow the exclusion of the most affected images from analysis. However, the criterion for exclusion is difficult to determine objectively and exclusion can lead to a suboptimal compromise between image quality and sample size. We provide an alternative, data-driven solution that assigns weights to each image, computed from an index of image quality using restricted maximum likelihood. We illustrate this method through the analysis of quantitative MRI data. The proposed method restores the validity of statistical tests, and performs near optimally in all brain regions, despite local effects of head motion. This method is amenable to the analysis of a broad type of MRI data and can accommodate any measure of image quality., (© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

We used the Mass Multivariate Method on structural, resting-state, and task-related fMRI data from two groups of patients with schizophrenia and depression in order to define several regions of significant relevance to the differential diagnosis of those conditions. The regions included the left planum polare (PP), the left opercular part of the inferior frontal gyrus (OpIFG), the medial orbital gyrus (MOrG), the posterior insula (PIns), and the parahippocampal gyrus (PHG). This study delivered evidence that a multimodal neuroimaging approach can potentially enhance the validity of psychiatric diagnoses. Structural, resting-state, or task-related functional MRI modalities cannot provide independent biomarkers. Further studies need to consider and implement a model of incremental validity combining clinical measures with different neuroimaging modalities to discriminate depressive disorders from schizophrenia. Biological signatures of disease on the level of neuroimaging are more likely to underpin broader nosological entities in psychiatry.

Aim: There is ongoing debate about the role of cortical and subcortical brain areas in force modulation. In a whole-brain approach, we sought to investigate the anatomical basis of grip force whilst acknowledging interindividual differences in connectivity patterns. We tested if brain lesion mapping in patients with unilateral motor deficits can inform whole-brain structural connectivity analysis in healthy controls to uncover the networks underlying grip force., Methods: Using magnetic resonance imaging (MRI) and whole-brain voxel-based morphometry in chronic stroke patients (n=55) and healthy controls (n=67), we identified the brain regions in both grey and white matter significantly associated with grip force strength. The resulting statistical parametric maps (SPMs) provided seed areas for whole-brain structural covariance analysis in a large-scale community dwelling cohort (n=977) that included beyond volume estimates, parameter maps sensitive to myelin, iron and tissue water content., Results: The SPMs showed symmetrical bilateral clusters of correlation between upper limb motor performance, basal ganglia, posterior insula and cortico-spinal tract. The covariance analysis with the seed areas derived from the SPMs demonstrated a widespread anatomical pattern of brain volume and tissue properties, including both cortical, subcortical nodes of motor networks and sensorimotor areas projections., Conclusion: We interpret our covariance findings as a biological signature of brain networks implicated in grip force. The data-driven definition of seed areas obtained from chronic stroke patients showed overlapping structural covariance patterns within cortico-subcortical motor networks across different tissue property estimates. This cumulative evidence lends face validity of our findings and their biological plausibility., (Copyright © 2021. Published by Elsevier Inc.)

Despite decades of successful treatment of therapy-resistant depression and major scientific advances in the field, our knowledge about electro-convulsive therapy's (ECT) mechanisms of action is still scarce. Building on strong empirical evidence for ECT-induced hippocampus anatomy changes, we sought to test the hypothesis that ECT has a differential impact along the hippocampus longitudinal axis. We acquired behavioural and brain anatomy magnetic resonance imaging (MRI) data in patients with depressive episode undergoing ECT (n = 9) or pharmacotherapy (n = 24) and healthy controls (n = 30) at two time points 3 months apart. Using whole-brain voxel-based statistical parametric mapping and topographic analysis focused on the hippocampus, we observed ECT-induced gradient of grey matter volume increase along the hippocampal longitudinal axis with predominant impact on its anterior portion. Clinical outcome measures showed strong correlations with both baseline volume and rate of ECT-induced change exclusively for the anterior, but not posterior hippocampus. We interpret our findings confined to the anterior hippocampus and amygdala as additional evidence of the regional specific impact of ECT that unfolds its beneficial effect on depression via the "limbic" system. Main limitations of the study are patients' polypharmacy, heterogeneity of psychiatric diagnosis, and long-time interval between scans.

A growing body of empirical evidence supports the notion of diverse neurobiological processes underlying learning-induced plasticity changes in the human brain. There are still open questions about how brain plasticity depends on cognitive task complexity, how it supports interactions between brain systems and with what temporal and spatial trajectory. We investigated brain and behavioural changes in sighted adults during 8-months training of tactile Braille reading whilst monitoring brain structure and function at 5 different time points. We adopted a novel multivariate approach that includes behavioural data and specific MRI protocols sensitive to tissue properties to assess local functional and structural and myelin changes over time. Our results show that while the reading network, located in the ventral occipitotemporal cortex, rapidly adapts to tactile input, sensory areas show changes in grey matter volume and intra-cortical myelin at different times. This approach has allowed us to examine and describe neuroplastic mechanisms underlying complex cognitive systems and their (sensory) inputs and (motor) outputs differentially, at a mesoscopic level., (Copyright © 2020. Published by Elsevier Inc.)

The Apolipoprotein E isoform E4 (ApoE4) is consistently associated with an elevated risk of developing late-onset Alzheimer's Disease (AD); however, less is known about the potential genetic modulation of the brain networks organization during prodromal stages like Mild Cognitive Impairment (MCI). To investigate this issue during this critical stage, we used a dataset with a cross-sectional sample of 253 MCI patients divided into ApoE4-positive (‛Carriers') and ApoE4-negative ('non-Carriers'). We estimated the cortical thickness (CT) from high-resolution T1-weighted structural magnetic images to calculate the correlation among anatomical regions across subjects and build the CT covariance networks (CT-Nets). The topological properties of CT-Nets were described through the graph theory approach. Specifically, our results showed a significant decrease in characteristic path length, clustering-index, local efficiency, global connectivity, modularity, and increased global efficiency for Carriers compared to non-Carriers. Overall, we found that ApoE4 in MCI shaped the topological organization of CT-Nets. Our results suggest that in the MCI stage, the ApoE4 disrupting the CT correlation between regions may be due to adaptive mechanisms to sustain the information transmission across distant brain regions to maintain the cognitive and behavioral abilities before the occurrence of the most severe symptoms.

There is evidence that gray matter networks are disrupted in Mild Cognitive Impairment (MCI) and associated with cognitive impairment and faster disease progression. However, it remains unknown how these alterations are related to the presence of Apolipoprotein E isoform E4 (ApoE4), the most prominent genetic risk factor for late-onset Alzheimer's disease (AD). To investigate this topic at the individual level, we explore the impact of ApoE4 and the disease progression on the Single-Subject Gray Matter Networks (SSGMNets) using the graph theory approach. Our data sample comprised 200 MCI patients selected from the ADNI database, classified as non-Converters and Converters (will progress into AD). Each group included 50 ApoE4-positive ('Carriers', ApoE4 + ) and 50 ApoE4-negative ('non-Carriers', ApoE4-). The SSGMNets were estimated from structural MRIs at two-time points: baseline and conversion. We investigated whether altered network topological measures at baseline and their rate of change (RoC) between baseline and conversion time points were associated with ApoE4 and disease progression. We also explored the correlation of SSGMNets attributes with general cognition score (MMSE), memory (ADNI-MEM), and CSF-derived biomarkers of AD (Aβ42, T-tau, and P-tau). Our results showed that ApoE4 and the disease progression modulated the global topological network properties independently but not in their RoC. MCI converters showed a lower clustering index in several regions associated with neurodegeneration in AD. The SSGMNets' topological organization was revealed to be able to predict cognitive and memory measures. The findings presented here suggest that SSGMNets could indeed be used to identify MCI ApoE4 Carriers with a high risk for AD progression., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Traditional psychiatric diagnosis has been overly reliant on either self-reported measures (introspection) or clinical rating scales (interviews). This produced the so-called explanatory gap with the bio-medical disciplines, such as neuroscience, which are supposed to deliver biological explanations of disease. In that context the neuro-biological and clinical assessment in psychiatry remained discrepant and incommensurable under conventional statistical frameworks. The emerging field of translational neuroimaging attempted to bridge the explanatory gap by means of simultaneous application of clinical assessment tools and functional magnetic resonance imaging, which also turned out to be problematic when analyzed with standard statistical methods. In order to overcome this problem our group designed a novel machine learning technique, multivariate linear method (MLM) which can capture convergent data from voxel-based morphometry, functional resting state and task-related neuroimaging and the relevant clinical measures. In this paper we report results from convergent cross-validation of biological signatures of disease in a sample of patients with schizophrenia as compared to depression. Our model provides evidence that the combination of the neuroimaging and clinical data in MLM analysis can inform the differential diagnosis in terms of incremental validity.

Background: Mesial temporal lobe epilepsy (TLE) is one of the most widespread neurological network disorders. Computational anatomy MRI studies demonstrate a robust pattern of cortical volume loss. Most statistical analyses provide information about localization of significant focal differences in a segregationist way. Multivariate Bayesian modeling provides a framework allowing inferences about inter-regional dependencies. We adopt this approach to answer following questions: Which structures within a pattern of dynamic epilepsy-associated brain anatomy reorganization best predict TLE pathology. Do these structures differ between TLE subtypes?, Methods: We acquire clinical and MRI data from TLE patients with and without hippocampus sclerosis (n = 128) additional to healthy volunteers (n = 120). MRI data were analyzed in the computational anatomy framework of SPM12 using classical mass-univariate analysis followed by multivariate Bayesian modeling., Results: After obtaining TLE-associated brain anatomy pattern, we estimate predictive power for disease and TLE subtypes using Bayesian model selection and comparison. We show that ipsilateral para-/hippocampal regions contribute most to disease-related differences between TLE and healthy controls independent of TLE laterality and subtype. Prefrontal cortical changes are more discriminative for left-sided TLE, whereas thalamus and temporal pole for right-sided TLE. The presence of hippocampus sclerosis was linked to stronger involvement of thalamus and temporal lobe regions; frontoparietal involvement was predominant in absence of sclerosis., Conclusions: Our topology inferences on brain anatomy demonstrate a differential contribution of structures within limbic and extralimbic circuits linked to main effects of TLE and hippocampal sclerosis. We interpret our results as evidence for TLE-related spatial modulation of anatomical networks., (© 2020 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify-CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD ( n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from "slight" to "significant" in 80% of the cases. Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology., (Copyright © 2020 Redolfi, De Francesco, Palesi, Galluzzi, Muscio, Castellazzi, Tiraboschi, Savini, Nigri, Bottini, Bruzzone, Ramusino, Ferraro, Gandini Wheeler-Kingshott, Tagliavini, Frisoni, Ryvlin, Demonet, Kherif, Cappa and D'Angelo.)

Introduction: There exists over the past decades a constant debate driven by controversies in the validity of psychiatric diagnosis. This debate is grounded in queries about both the validity and evidence strength of clinical measures. Materials and Methods: The objective of the study is to construct a bottom-up unsupervised machine learning approach, where the brain signatures identified by three principal components based on activations yielded from the three kinds of diagnostically relevant stimuli are used in order to produce cross-validation markers which may effectively predict the variance on the level of clinical populations and eventually delineate diagnostic and classification groups. The stimuli represent items from a paranoid-depressive self-evaluation scale, administered simultaneously with functional magnetic resonance imaging (fMRI). Results: We have been able to separate the two investigated clinical entities - schizophrenia and recurrent depression by use of multivariate linear model and principal component analysis. Following the individual and group MLM, we identified the three brain patterns that summarized all the individual variabilities of the individual brain patterns. Discussion: This is a confirmation of the possibility to achieve bottom-up classification of mental disorders, by use of the brain signatures relevant to clinical evaluation tests., (Copyright © 2019 Stoyanov, Kandilarova, Paunova, Barranco Garcia, Latypova and Kherif.)

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., (© 2019 Wiley Periodicals, Inc.)

The hMRI toolbox is an open-source toolbox for the calculation of quantitative MRI parameter maps from a series of weighted imaging data, and optionally additional calibration data. The multi-parameter mapping (MPM) protocol, incorporating calibration data to correct for spatial variation in the scanner's transmit and receive fields, is the most complete protocol that can be handled by the toolbox. Here we present a dataset acquired with such a full MPM protocol, which is made freely available to be used as a tutorial by following instructions provided on the associated toolbox wiki pages, which can be found at http://hMRI.info, and following the theory described in: hMRI - A toolbox for quantitative MRI in neuroscience and clinical research [1].

There is much controversy about the optimal trade-off between blood-oxygen-level-dependent (BOLD) sensitivity and spatial precision in experiments on brain's topology properties using functional magnetic resonance imaging (fMRI). The sparse empirical evidence and regional specificity of these interactions pose a practical burden for the choice of imaging protocol parameters. Here, we test in a motor somatotopy experiment the impact of fMRI spatial resolution on differentiation between body part representations in cortex and subcortical structures. Motor somatotopy patterns were obtained in a block-design paradigm and visually cued movements of face, upper and lower limbs at 1.5, 2, and 3 mm spatial resolution. The degree of segregation of the body parts' spatial representations was estimated using a pattern component model. In cortical areas, we observed the same level of segregation between somatotopy maps across all three resolutions. In subcortical areas the degree of effective similarity between spatial representations was significantly impacted by the image resolution. The 1.5 mm 3D EPI and 3 mm 2D EPI protocols led to higher segregation between motor representations compared to the 2 mm 3D EPI protocol. This finding could not be attributed to differential BOLD sensitivity or delineation of functional areas alone and suggests a crucial role of the image encoding scheme - i.e., 2D vs. 3D EPI. Our study contributes to the field by providing empirical evidence about the impact of acquisition protocols for the delineation of somatotopic areas in cortical and sub-cortical brain regions.

The human brain undergoes dramatic structural change over the life span. In a large imaging cohort of 801 individuals aged 7-84 years, we applied quantitative relaxometry and diffusion microstructure imaging in combination with diffusion tractography to investigate tissue property dynamics across the human life span. Significant nonlinear aging effects were consistently observed across tracts and tissue measures. The age at which white matter (WM) fascicles attain peak maturation varies substantially across tissue measurements and tracts. These observations of heterochronicity and spatial heterogeneity of tract maturation highlight the importance of using multiple tissue measurements to investigate each region of the WM. Our data further provide additional quantitative evidence in support of the last-in-first-out retrogenesis hypothesis of aging, demonstrating a strong correlational relationship between peak maturational timing and the extent of quadratic measurement differences across the life span for the most myelin sensitive measures. These findings present an important baseline from which to assess divergence from normative aging trends in developmental and degenerative disorders, and to further investigate the mechanisms connecting WM microstructure to cognition., (© 2019 Wiley Periodicals, Inc.)

Networks of anatomical covariance have been widely used to study connectivity patterns in both normal and pathological brains based on the concurrent changes of morphometric measures (i.e., cortical thickness) between brain structures across subjects (Evans, ). However, the existence of networks of microstructural changes within brain tissue has been largely unexplored so far. In this article, we studied in vivo the concurrent myelination processes among brain anatomical structures that gathered together emerge to form nonrandom networks. We name these "networks of myelin covariance" (Myelin-Nets). The Myelin-Nets were built from quantitative Magnetization Transfer data-an in-vivo magnetic resonance imaging (MRI) marker of myelin content. The synchronicity of the variations in myelin content between anatomical regions was measured by computing the Pearson's correlation coefficient. We were especially interested in elucidating the effect of age on the topological organization of the Myelin-Nets. We therefore selected two age groups: Young-Age (20-31 years old) and Old-Age (60-71 years old) and a pool of participants from 48 to 87 years old for a Myelin-Nets aging trajectory study. We found that the topological organization of the Myelin-Nets is strongly shaped by aging processes. The global myelin correlation strength, between homologous regions and locally in different brain lobes, showed a significant dependence on age. Interestingly, we also showed that the aging process modulates the resilience of the Myelin-Nets to damage of principal network structures. In summary, this work sheds light on the organizational principles driving myelination and myelin degeneration in brain gray matter and how such patterns are modulated by aging., (© 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.)

Introduction: Certain personality traits are associated with higher risk of Alzheimer's disease, similar to cognitive impairment. The identification of biological markers associated with personality in mild cognitive impairment could advance the early detection of Alzheimer's disease., Methods: We used hierarchical multivariate linear models to quantify the interaction between personality traits, state of cognitive impairment, and MRI biomarkers (gray matter brain volume, gray matter mean water diffusion) in the medial temporal lobe (MTL)., Results: Over and above a main effect of cognitive state, the multivariate linear model showed significant interaction between cognitive state and personality traits predicting MTL abnormality. The interaction effect was mainly driven by neuroticism and its facets (anxiety, depression, and stress) and was associated with right-left asymmetry and an anterior to posterior gradient in the MTL., Discussion: Our results support the hypothesis that personality traits can alter the vulnerability and pathoplasticity of disease and therefore modulate related biomarker expression.

The high gray-white matter contrast and spatial resolution provided by T1-weighted magnetic resonance imaging (MRI) has made it a widely used imaging protocol for computational anatomy studies of the brain. While the image intensity in T1-weighted images is predominantly driven by T1, other MRI parameters affect the image contrast, and hence brain morphological measures derived from the data. Because MRI parameters are correlates of different histological properties of brain tissue, this mixed contribution hampers the neurobiological interpretation of morphometry findings, an issue which remains largely ignored in the community. We acquired quantitative maps of the MRI parameters that determine signal intensities in T1-weighted images (R1 (=1/T1), R2 *, and PD) in a large cohort of healthy subjects (n = 120, aged 18-87 years). Synthetic T1-weighted images were calculated from these quantitative maps and used to extract morphometry features-gray matter volume and cortical thickness. We observed significant variations in morphometry measures obtained from synthetic images derived from different subsets of MRI parameters. We also detected a modulation of these variations by age. Our findings highlight the impact of microstructural properties of brain tissue-myelination, iron, and water content-on automated measures of brain morphology and show that microstructural tissue changes might lead to the detection of spurious morphological changes in computational anatomy studies. They motivate a review of previous morphological results obtained from standard anatomical MRI images and highlight the value of quantitative MRI data for the inference of microscopic tissue changes in the healthy and diseased brain. Hum Brain Mapp 37:1801-1815, 2016. © 2016 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc., (© 2016 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.)

Despite advances in understanding basic organizational principles of the human basal ganglia, accurate in vivo assessment of their anatomical properties is essential to improve early diagnosis in disorders with corticosubcortical pathology and optimize target planning in deep brain stimulation. Main goal of this study was the detailed topological characterization of limbic, associative, and motor subdivisions of the subthalamic nucleus (STN) in relation to corresponding corticosubcortical circuits. To this aim, we used magnetic resonance imaging and investigated independently anatomical connectivity via white matter tracts next to brain tissue properties. On the basis of probabilistic diffusion tractography we identified STN subregions with predominantly motor, associative, and limbic connectivity. We then computed for each of the nonoverlapping STN subregions the covariance between local brain tissue properties and the rest of the brain using high-resolution maps of magnetization transfer (MT) saturation and longitudinal (R1) and transverse relaxation rate (R2*). The demonstrated spatial distribution pattern of covariance between brain tissue properties linked to myelin (R1 and MT) and iron (R2*) content clearly segregates between motor and limbic basal ganglia circuits. We interpret the demonstrated covariance pattern as evidence for shared tissue properties within a functional circuit, which is closely linked to its function. Our findings open new possibilities for investigation of changes in the established covariance pattern aiming at accurate diagnosis of basal ganglia disorders and prediction of treatment outcome., (Copyright © 2014 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.)

In this article we provide a comprehensive literature review on the in vivo assessment of use-dependant brain structure changes in humans using magnetic resonance imaging (MRI) and computational anatomy. We highlight the recent findings in this field that allow the uncovering of the basic principles behind brain plasticity in light of the existing theoretical models at various scales of observation. Given the current lack of in-depth understanding of the neurobiological basis of brain structure changes we emphasize the necessity of a paradigm shift in the investigation and interpretation of use-dependent brain plasticity. Novel quantitative MRI acquisition techniques provide access to brain tissue microstructural properties (e.g., myelin, iron, and water content) in-vivo, thereby allowing unprecedented specific insights into the mechanisms underlying brain plasticity. These quantitative MRI techniques require novel methods for image processing and analysis of longitudinal data allowing for straightforward interpretation and causality inferences.

Multi-centre data repositories like the Alzheimer's Disease Neuroimaging Initiative (ADNI) offer a unique research platform, but pose questions concerning comparability of results when using a range of imaging protocols and data processing algorithms. The variability is mainly due to the non-quantitative character of the widely used structural T1-weighted magnetic resonance (MR) images. Although the stability of the main effect of Alzheimer's disease (AD) on brain structure across platforms and field strength has been addressed in previous studies using multi-site MR images, there are only sparse empirically-based recommendations for processing and analysis of pooled multi-centre structural MR data acquired at different magnetic field strengths (MFS). Aiming to minimise potential systematic bias when using ADNI data we investigate the specific contributions of spatial registration strategies and the impact of MFS on voxel-based morphometry in AD. We perform a whole-brain analysis within the framework of Statistical Parametric Mapping, testing for main effects of various diffeomorphic spatial registration strategies, of MFS and their interaction with disease status. Beyond the confirmation of medial temporal lobe volume loss in AD, we detect a significant impact of spatial registration strategy on estimation of AD related atrophy. Additionally, we report a significant effect of MFS on the assessment of brain anatomy (i) in the cerebellum, (ii) the precentral gyrus and (iii) the thalamus bilaterally, showing no interaction with the disease status. We provide empirical evidence in support of pooling data in multi-centre VBM studies irrespective of disease status or MFS., (Copyright © 2013 Wiley Periodicals, Inc.)

There remains much scientific, clinical, and ethical controversy concerning the use of electroconvulsive therapy (ECT) for psychiatric disorders stemming from a lack of information and knowledge about how such treatment might work, given its nonspecific and spatially unfocused nature. The mode of action of ECT has even been ascribed to a "barbaric" form of placebo effect. Here we show differential, highly specific, spatially distributed effects of ECT on regional brain structure in two populations: patients with unipolar or bipolar disorder. Unipolar and bipolar disorders respond differentially to ECT and the associated local brain-volume changes, which occur in areas previously associated with these diseases, correlate with symptom severity and the therapeutic effect. Our unique evidence shows that electrophysical therapeutic effects, although applied generally, take on regional significance through interactions with brain pathophysiology.

The early diagnostic value of glucose hypometabolism and atrophy as potential neuroimaging biomarkers of mild cognitive impairment (MCI) and Alzheimer's disease (AD) have been extensively explored using [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) and structural magnetic resonance imaging (MRI). The vast majority of previous imaging studies neglected the effects of single factors, such as age, symptom severity or time to conversion in MCI thus limiting generalisability of results across studies. Here, we investigated the impact of these factors on metabolic and structural differences. FDG-PET and MRI data from AD patients (n = 80), MCI converters (n = 65) and MCI non-converters (n = 64) were compared to data of healthy subjects (n = 79). All patient groups were split into subgroups by age, time to conversion (for MCI), or symptom severity and compared to the control group. AD patients showed a strongly age-dependent pattern, with younger patients showing significantly more extensive reductions in gray matter volume and glucose utilisation. In the MCI converter group, the amount of glucose utilisation reduction was linked to the time to conversion but not to atrophy. Our findings indicate that FDG-PET might be more closely linked to future cognitive decline whilst MRI being more closely related to the current cognitive state reflects potentially irreversible damage.