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Introduction: Older-age bipolar disorder (OABD) may involve neurocognitive decline and behavioral disturbances that could share features with the behavioral variant of frontotemporal dementia (bvFTD), making the differential diagnosis difficult in cases of suspected dementia.Objective: To compare the neuropsychological profile, brain morphometry, and structural connectivity patterns between patients diagnosed with bvFTD, patients classified as OABD with an early onset of the disease (EO-OABD), and healthy controls (HC).Methods: bvFTD patients (n = 25, age: 66 ± 7, female: 64%, disease duration: 6 ± 4 years), EO-OABD patients (n = 17, age: 65 ± 9, female: 71%, disease duration: 38 ± 8 years), and HC (n = 28, age: 62 ± 7, female: 64%) were evaluated through neuropsychological tests concerning attention, memory, executive function, praxis, and language. Brain morphometry was analyzed through surface-based morphometry (SBM), while structural brain connectivity was assessed through diffusion tensor imaging (DTI).Results: Both bvFTD and EO-OABD patients showed lower performance in neuropsychological tests of attention, verbal fluency, working memory, verbal memory, and praxis than HC. Comparisons between EO-OABD and bvFTD showed differences limited to cognitive flexibility delayed recall and intrusion errors in the memory test. SBM analysis demonstrated that several frontal, temporal, and parietal regions were altered in both bvFTD and EO-OABD compared to HC. In contrast, comparisons between bvFTD and EO-OABD evidenced differences exclusively in the right temporal pole and the left entorhinal cortex. DTI analysis showed alterations in association and projection fibers in both EO-OABD and bvFTD patients compared to HC. Commissural fibers were found to be particularly affected in EO-OABD. The middle cerebellar peduncle and the pontine crossing tract were exclusively altered in bvFTD. There were no significant differences in DTI analysis between EO-OABD and bvFTD.Discussion: EO-OABD and bvFTD may share an overlap in cognitive, brain morphometry, and structural connectivity profiles that could reflect common underlying mechanisms, even though the etiology of each disease can be different and multifactorial.

Background: Patients with Parkinson's disease (PD) and progressive supranuclear palsy Richardson's syndrome (PSP-RS) often show overlapping clinical features, leading to misdiagnoses. The objective of this study was to investigate the feasibility and utility of using multi-modal MRI datasets for an automatic differentiation of PD patients, PSP-RS patients, and healthy control (HC) subjects.Material and Methods: T1-weighted, T2-weighted, and diffusion-tensor (DTI) MRI datasets from 45 PD patients, 20 PSP-RS patients, and 38 HC subjects were available for this study. Using an atlas-based approach, regional values of brain morphology (T1-weighted), brain iron metabolism (T2-weighted), and microstructural integrity (DTI) were measured and employed for feature selection and subsequent classification using combinations of various established machine learning methods.Results: The optimal machine learning model using regional morphology features only achieved a classification accuracy of 65% (67/103 correct classifications) differentiating PD patients, PSP-RS patients, and HC subjects. The optimal machine learning model using only quantitative T2 values performed slightly better and achieved an accuracy of 75.7% (78/103). The optimal classifier using DTI features alone performed considerably better with 95.1% accuracy (98/103). The optimal multi-modal classifier using all features also achieved an accuracy of 95.1% but required more features and achieved a slightly lower F1-score compared to the optimal model using DTI features alone.Conclusion: Machine learning models using multi-modal MRI perform significantly better than uni-modal machine learning models using morphological parameters based on T1-weighted MRI datasets alone or brain iron metabolism markers based on T2-weighted MRI datasets alone. However, machine learnig models using regional brain microstructural integrity metrics computed from DTI datasets perform similar to the optimal multi-modal machine learning model. Thus, given the results from this study cohort, it appears that morphology and brain iron metabolism markers may not provide additional value for classification compared to using DTI metrics alone.

Alzheimer's disease (AD) continuum is defined as a cascade of several neuropathological processes that can be measured using biomarkers, such as cerebrospinal fluid (CSF) levels of Aß, p-tau, and t-tau. In parallel, brain anatomy can be characterized through imaging techniques, such as magnetic resonance imaging (MRI). In this work we relate both sets of measurements and seek associations between biomarkers and the brain structure that can be indicative of AD progression. The goal is to uncover underlying multivariate effects of AD pathology on regional brain morphological information. For this purpose, we used the projection to latent structures (PLS) method. Using PLS, we found a low dimensional latent space that best describes the covariance between both sets of measurements on the same subjects. Possible confounder effects (age and sex) on brain morphology are included in the model and regressed out using an orthogonal PLS model. We looked for statistically significant correlations between brain morphology and CSF biomarkers that explain part of the volumetric variance at each region-of-interest (ROI). Furthermore, we used a clustering technique to discover a small set of CSF-related patterns describing the AD continuum. We applied this technique to the study of subjects in the whole AD continuum, from the pre-clinical asymptomatic stages all the way through to the symptomatic groups. Subsequent analyses involved splitting the course of the disease into diagnostic categories: cognitively unimpaired subjects (CU), mild cognitively impaired subjects (MCI), and subjects with dementia (AD-dementia), where all symptoms were due to AD. This work has been partially supported by the project MALEGRA TEC2016-75976-R financed by the Spanish Ministerio de Economía y Competitividad and the European Regional Development Fund (ERDF). AC was supported by the Spanish Ministerio de Educación, Cultura y Deporte FPU Research Fellowship. JG holds a Ramón y Cajal fellowship (RYC-2013-13054).

This chapter overviews research neuroimaging findings of patients with medication-overuse headache (MOH). Results indicate; (i) correlations between neuropathology and medication-overuse; (ii) changes in brain morphology and cortical function; and (iii) brain recovery subsequent to withdrawal of medication that was overused. Results of this narrative review indicate exacerbated brain structural and functional changes in regions of the pain-matrix and in regions of the mesocortical-limbic circuit in patients with MOH compared to patients with migraine or compared to healthy controls. Modification of brain morphology as well as an association between brain recovery and medication withdrawal suggest that the MOH disease process involves state (brain modification) and trait-like (brain adaptation and recovery) neuromechanisms.

Major hardware/software changes to MRI platforms, either planned or unplanned, will almost invariably occur in longitudinal studies. Our objective was to assess the resulting variability on relevant imaging measurements in such context, specifically for three Siemens Healthcare Magnetom Trio upgrades to the Prismafit platform. We report data acquired on three healthy volunteers scanned before and after three different platform upgrades. We assessed differences in image signal [contrast-to-noise ratio (CNR)] on T1-weighted images (T1w) and fluid-attenuated inversion recovery images (FLAIR); brain morphometry on T1w image; and small vessel disease (white matter hyperintensities; WMH) on FLAIR image. Prismafit upgrade resulted in higher (30%) and more variable neocortical CNR and larger brain volume and thickness mainly in frontal areas. A significant relationship was observed between neocortical CNR and neocortical volume. For FLAIR images, no significant CNR difference was observed, but WMH volumes were significantly smaller (-68%) after Prismafit upgrade, when compared to results on the Magnetom Trio. Together, these results indicate that Prismafit upgrade significantly influenced image signal, brain morphometry measures and small vessel diseases measures and that these effects need to be taken into account when analyzing results from any longitudinal study undergoing similar changes.

Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. By combining longitudinal MRI-based brain morphometry and brain age estimation using machine learning, we tested the hypothesis that MS patients have higher brain age relative to chronological age than healthy controls (HC) and that longitudinal rate of brain aging in MS patients is associated with clinical course and severity. Seventy-six MS patients [71% females, mean age 34.8 years (range 21-49) at inclusion] were examined with brain MRI at three time points with a mean total follow up period of 4.4 years (±0.4 years). We used additional cross-sectional MRI data from 235 HC for case-control comparison. We applied a machine learning model trained on an independent set of 3,208 HC to estimate individual brain age and to calculate the difference between estimated and chronological age, termed brain age gap (BAG). We also assessed the longitudinal change rate in BAG in individuals with MS. MS patients showed significantly higher BAG (4.4 ± 6.6 years) compared to HC (Cohen's D = 0.69, p = 4.0 × 10-6). Longitudinal estimates of BAG in MS patients showed high reliability and suggested an accelerated rate of brain aging corresponding to an annual increase of 0.41 (SE = 0.15) years compared to chronological aging (p = 0.008). Multiple regression analyses revealed higher rate of brain aging in patients with more brain atrophy (Cohen's D = 0.86, p = 4.3 × 10-15) and increased white matter lesion load (WMLL) (Cohen's D = 0.55, p = 0.015). On average, patients with MS had significantly higher BAG compared to HC. Progressive brain aging in patients with MS was related to brain atrophy and increased WMLL. No significant clinical associations were found in our sample, future studies are warranted on this matter. Brain age estimation is a promising method for evaluation of subtle brain changes in MS, which is important for predicting clinical outcome and guide choice of intervention.

Changes in brain morphometry have been extensively reported in various studies examining the effects of chronic alcohol use in alcohol-dependent patients. Such studies were able to confirm the association between chronic alcohol use and volumetric reductions in subcortical structures using FSL (FMRIB software library). However, each study that utilized FSL had different sets of subcortical structures that showed significant volumetric reduction. First, we aimed to investigate the reproducibility of using FSL to assess volumetric differences of subcortical structures between alcohol-dependent patients and control subjects. Second, we aimed to use Vertex analysis, a less utilized program, to visually inspect 3D meshes of subcortical structures and observe significant shape abnormalities that occurred in each subcortical structure. Vertex analysis results from the hippocampus and thalamus were overlaid on top of their respective subregional atlases to further pinpoint the subregional locations where shape abnormalities occurred. We analyzed the volumes of 14 subcortical structures (bilateral thalamus, caudate, putamen, globus pallidus, hippocampus, amygdala, nucleus accumbens) in 21 alcohol-dependent subjects and 21 healthy controls using images acquired with 3T MRI. The images were run through various programs found in FSL, such as SIENAX, FIRST, and Vertex analysis. We found that in alcohol-dependent patients, the bilateral thalamus (left: p < 0.01, right: p = 0.01), bilateral putamen (left: p = 0.02, right: p < 0.01), right globus pallidus (p < 0.01), bilateral hippocampus (left: p = 0.05, right: p = 0.03) and bilateral nucleus accumbens (left: p = 0.05, right: p = 0.03) were significantly reduced compared to the corresponding subcortical structures of healthy controls. With vertex analysis, we observed surface reductions of the following hippocampal subfields: Presubiculum, hippocampal tail, hippocampal molecular layer, hippocampal fissure, fimbria, and CA3. We reproduced the assessment made in previous studies that reductions in subcortical volume were negatively associated with alcohol dependence by using the FMRIB Software Library. In addition, we identified the subfields of the thalamus and hippocampus that showed volumetric reduction.

Dopamine replacement therapy in the form of levodopa results in a significant proportion of patients with Parkinson's disease (PD) developing debilitating dyskinesia. This significantly complicates further treatment and negatively impacts patient quality of life. A greater understanding of the neurobiological mechanisms underlying levodopa-induced dyskinesia (LID) is therefore crucial to develop new treatments to prevent or mitigate LID. Such investigations in humans are largely confined to assessment of neurochemical and cerebrovascular blood flow changes using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). However, recent evidence suggests that LID is associated with specific morphological changes in the frontal cortex and midbrain, detectable by structural MRI and voxel-based morphometry (VBM). Current human neuroimaging methods however lack sufficient resolution to reveal the biological mechanism driving these morphological changes at the cellular level. In contrast, there is a wealth of literature from well-established rodent models of LID documenting detailed post-mortem cellular and molecular measurements. The combination therefore of advanced neuroimaging methods and rodent LID models offers an exciting opportunity to bridge these currently disparate areas of research. To highlight this opportunity, in this mini-review, we provide an overview of the current clinical evidence for morphological changes in the brain associated with LID and identify potential cellular mechanisms as suggested from human and animal studies. We then suggest a framework for combining small animal MRI imaging with rodent models of LID, which may provide important mechanistic insights into the neurobiology of LID.

Biomarkers derived from brain magnetic resonance imaging have promise in being able to assist in the clinical diagnosis of brain pathologies. These have been used in many studies in which the goal has been to distinguish between pathologies such as Alzheimer's disease and healthy aging. However, other dementias, in particular, Fronto- temporal dementia, also present overlapping pathological brain morphometry patterns. Hence, a classifier that can discriminate morphometric features from a brain MRI from the three classes of normal aging, Alzheimer’s disease (AD) and Frontotemporal dementia (FTD) would offer considerable utility in aiding in correct group identification. Compared to the conventional use of multiple pair-wise binary classifiers that learn to discriminate between two classes at each stage, we propose a single three-way classification system that can discriminate between three classes at the same time. We present a novel classifier that is able to perform a three-class discrimination test for discriminating among AD, FTD and normal controls using volumes, shape invariants and local displacements (3 features) of hippocampi and lateral ventricles (2 structures times two hemispheres individually) obtained from brain MR images. In order to quantify its utility in correct discrimination, we optimize the three-class classifier on a training set and evaluate its performance using a separate test set. This is a novel, first-of-its-kind comparative study of multiple individual biomarkers in a three-class setting. Our results demonstrate that local atrophy features in lateral ventricles offer the potential to be a biomarker in discriminating among Alzheimer’s disease, frontotemporal dementia and normal controls in a 3-class setting for individual patient classification.

Introduction: Obstructive sleep apnea syndrome (OSAS) is a frequent breathing disorder occurring during sleep that is characterized by recurrent hypoxic episodes and sleep fragmentation. It remains unclear whether OSAS leads to structural brain changes, and if so, in which brain regions. Brain region-specific gray and white matter volume (GMV and WMV) changes can be measured with voxel-based morphometry (VBM). The aims of this study were to use VBM to analyze GMV and WMV in untreated OSAS patients compared to healthy controls (HC); examine the impact of OSAS-related variables (nocturnal hypoxemia duration and sleep fragmentation index) on GMV and WMV; and assess the effects of therapeutic versus sham continuous positive airway pressure (CPAP). We discuss our results in light of previous findings and provide a comprehensive literature review. Methods: Twenty-seven treatment-naïve male patients with moderate to severe OSAS and seven healthy age- and education-matched control subjects (HC) were recruited. After a baseline fMRI scan, patients randomly received either active (therapeutic, n=14) or sham (subtherapeutic, n=13) nasal CPAP treatment for 2 months. Results: Significant negative correlations were observed between nocturnal hypoxemia duration and GMV in bilateral lateral temporal regions. No differences in GMV or WMV were found between OSAS patients and HC, and no differences between CPAP versus sham CPAP treatment effects in OSAS patients. Conclusion: It appears that considering VBM GMV changes there is little difference between OSAS patients and HC. The largest VBM study to date indicates structural changes in the lateral aspect of the temporal lobe, which also showed a significant negative correlation with nocturnal hypoxemia duration in our study. This finding suggests an association between the effect of nocturnal hypoxemia and decreased GMV in OSAS patients.

Structural brain alterations in chronic pain conditions remain incompletely understood, especially in chronic visceral pain. Patients with chronic-inflammatory or functional bowel disorders experience recurring abdominal pain in concert with other gastrointestinal symptoms, such as altered bowel habits, which are often exacerbated by stress. Despite growing interest in the gut-brain axis and its underlying neural mechanisms in health and disease, abnormal brain morphology and possible associations with visceral symptom severity and chronic stress remain unclear. We accomplished parallelized whole-brain voxel-based morphometry analyses in two patient cohorts with chronic visceral pain, i.e., ulcerative colitis in remission and irritable bowel syndrome, and healthy individuals. In addition to analyzing changes in gray matter volume (GMV) in each patient cohort vs. age-matched healthy controls using analysis of covariance (ANCOVA), multiple regression analyses were conducted to assess correlations between GMV and symptom severity and chronic stress, respectively. ANCOVA revealed reduced GMV in frontal cortex and anterior insula in ulcerative colitis compared to healthy controls, suggesting alterations in the central autonomic and salience networks, which could however not be confirmed in supplemental analyses which rigorously accounted for group differences in the distribution of sex. In irritable bowel syndrome, more widespread differences from healthy controls were observed, comprising both decreased and increased GMV within the sensorimotor, central executive and default mode networks. Associations between visceral symptoms and GMV within frontal regions were altered in both patient groups, supporting a role of the central executive network across visceral pain conditions. Correlations with chronic stress, on the other hand, were only found for irritable bowel syndrome, encompassing numerous brain regions and networks. Together, these findings complement and expand existing brain imaging evidence in chronic visceral pain, supporting partly distinct alterations in brain morphology in patients with chronic-inflammatory and functional bowel disorders despite considerable overlap in symptoms and comorbidities. First evidence pointing to correlations with chronic stress in irritable bowel syndrome inspires future translational studies to elucidate the mechanisms underlying the interconnections of stress, visceral pain and neural mechanisms of the gut-brain axis.

Valproate is a drug widely used to treat epilepsy, bipolar disorder, and occasionally to prevent migraine headache. Despite its clinical efficacy, prenatal exposure to valproate is associated with neurodevelopmental impairments and its use in children and adults was associated with rare cases of reversible brain atrophy and ventricular enlargement. To determine whether valproate use is related with structural brain changes we examined through a cross-sectional study cortical and subcortical structures in a group of 152 people with epilepsy and a normal clinical brain MRI. Patients were grouped into those currently using valproate (n = 54), those taking drugs other than valproate (n = 47), and drug-naive patients (n = 51) at the time of MRI, irrespectively of their epilepsy syndrome. Cortical thickness and subcortical volumes were analyzed using Freesurfer, version 5.0. Subjects exposed to valproate (either in mono- or polytherapy) showed reduced cortical thickness in the occipital lobe, more precisely in the cuneus bilaterally, in the left lingual gyrus, and in left and right pericalcarine gyri when compared to patients who used other antiepileptic drugs, to drug-naive epilepsy patients, and to healthy controls. Considering the subgroup of patients using valproate monotherapy (n = 25), both comparisons with healthy controls and drug-naive groups confirmed occipital lobe cortical thickness reduction. Moreover, patients using valproate showed increased left and right lateral ventricle volume compared to all other groups. Notably, subjects who were non-valproate users at the time of MRI, but who had valproate exposure in the past (n = 27) did not show these cortical or subcortical brain changes. Cortical changes in the posterior cortex, particularly in the visual cortex, and ventricular enlargement, are present in people with epilepsy using valproate, independently from clinical and demographical variables. These findings are relevant both for the efficacy and adverse events profile of valproate use in people with epilepsy.

Motivation: Brain morphometry from magnetic resonance imaging (MRI) is a promising neuroimaging biomarker for the non-invasive diagnosis and monitoring of neurodegenerative and neurological disorders. Current tools for brain morphometry often come with a high computational burden, making them hard to use in clinical routine, where time is often an issue. We propose a deep learning-based approach to predict the volumes of anatomically delineated subcortical regions of interest (ROI), and mean thicknesses and curvatures of cortical parcellations directly from T1-weighted MRI. Advantages are the timely availability of results while maintaining a clinically relevant accuracy. Materials and Methods: An anonymized dataset of 574 subjects (443 healthy controls and 131 patients with epilepsy) was used for the supervised training of a convolutional neural network (CNN). A silver-standard ground truth was generated with FreeSurfer 6.0. Results: The CNN predicts a total of 165 morphometric measures directly from raw MR images. Analysis of the results using intraclass correlation coefficients showed, in general, good correlation with FreeSurfer generated ground truth data, with some of the regions nearly reaching human inter-rater performance (ICC > 0.75). Cortical thicknesses predicted by the CNN showed cross-sectional annual age-related gray matter atrophy rates both globally (thickness change of -0.004 mm/year) and regionally in agreement with the literature. A statistical test to dichotomize patients with epilepsy from healthy controls revealed similar effect sizes for structures affecting all subtypes as reported in a large-scale epilepsy study. Conclusions: We demonstrate the general feasibility of using deep learning to estimate human brain morphometry directly from T1-weighted MRI within seconds. A comparison of the results to other publications shows accuracies of comparable magnitudes for the subcortical volumes and cortical thicknesses.