Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency.

With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have been developed to de-identify imaging data using blurring, defacing or refacing. Completely removing facial structures provides the best re-identification protection but can significantly impact post-processing steps, like brain morphometry. As an alternative, refacing methods that replace individual facial structures with generic templates have a lower effect on the geometry and intensity distribution of original scans, and are able to provide more consistent post-processing results by the price of higher re-identification risk and computational complexity. In the current study, we propose a novel method for anonymized face generation for defaced 3D T1-weighted scans based on a 3D conditional generative adversarial network. To evaluate the performance of the proposed de-identification tool, a comparative study was conducted between several existing defacing and refacing tools, with two different segmentation algorithms (FAST and Morphobox). The aim was to evaluate (i) impact on brain morphometry reproducibility, (ii) re-identification risk, (iii) balance between (i) and (ii), and (iv) the processing time. The proposed method takes 9 s for face generation and is suitable for recovering consistent post-processing results after defacing.

Inter-regional correlation estimators for functional magnetic resonance imaging.

Functional magnetic resonance imaging (fMRI) functional connectivity between brain regions is often computed using parcellations defined by functional or structural atlases. Typically, some kind of voxel averaging is performed to obtain a single temporal correlation estimate per region pair. However, several estimators can be defined for this task, with various assumptions and degrees of robustness to local noise, global noise, and region size. In this paper, we systematically present and study the properties of 9 different functional connectivity estimators taking into account the spatial structure of fMRI data, based on a simple fMRI data spatial model. These include 3 existing estimators and 6 novel estimators. We demonstrate the empirical properties of the estimators using synthetic, animal, and human data, in terms of graph structure, repeatability and reproducibility, discriminability, dependence on region size, as well as local and global noise robustness. We prove analytically the link between regional intra-correlation and inter-region correlation, and show that the choice of estimator has a strong influence on inter-correlation values. Some estimators, including the commonly used correlation of averages (ca), are positively biased, and have more dependence to region size and intra-correlation than robust alternatives, resulting in spatially-dependent bias. We define the new local correlation of averages estimator with better theoretical guarantees, lower bias, significantly lower dependence on region size (Spearman correlation 0.40 vs 0.55, paired t-test T=27.2, p=1.1e-47), at negligible cost to discriminative power, compared to the ca estimator. The difference in connectivity pattern between the estimators is not distributed uniformly throughout the brain, but rather shows a clear ventral-dorsal gradient, suggesting that region size and intra-correlation plays an important role in shaping functional networks defined using the ca estimator, and leading to non-trivial differences in their connectivity structure. We provide an open source R package and equivalent Python implementation to facilitate the use of the new estimators, together with preprocessed rat time-series.

Preparing for a Second Attack: A Lesion Simulation Study on Network Resilience After Stroke.

BACKGROUND: Does the brain become more resilient after a first stroke to reduce the consequences of a new lesion? Although recurrent strokes are a major clinical issue, whether and how the brain prepares for a second attack is unknown. This is due to the difficulties to obtain an appropriate dataset of stroke patients with comparable lesions, imaged at the same interval after onset. Furthermore, timing of the recurrent event remains unpredictable. METHODS: Here, we used a novel clinical lesion simulation approach to test the hypothesis that resilience in brain networks increases during stroke recovery. Sixteen highly selected patients with a lesion restricted to the primary motor cortex were recruited. At 3 time points of the index event (10 days, 3 weeks, 3 months), we mimicked recurrent infarcts by deletion of nodes in brain networks (resting-state functional magnetic resonance imaging). Graph measures were applied to determine resilience (global efficiency after attack) and wiring cost (mean degree) of the network. RESULTS: At 10 days and 3 weeks after stroke, resilience was similar in patients and controls. However, at 3 months, although motor function had fully recovered, resilience to clinically representative simulated lesions was higher compared to controls (cortical lesion P=0.012; subcortical: P=0.009; cortico-subcortical: P=0.009). Similar results were found after random (P=0.012) and targeted (P=0.015) attacks. CONCLUSIONS: Our results suggest that, in this highly selected cohort of patients with lesions restricted to the primary motor cortex, brain networks reconfigure to increase resilience to future insults. Lesion simulation is an innovative approach, which may have major implications for stroke therapy. Individualized neuromodulation strategies could be developed to foster resilient network reconfigurations after a first stroke to limit the consequences of future attacks.

Systemic and central nervous system neuroinflammatory signatures of neuropsychiatric symptoms and related cognitive decline in older people.

BACKGROUND: Neuroinflammation may contribute to psychiatric symptoms in older people, in particular in the context of Alzheimer's disease (AD). We sought to identify systemic and central nervous system (CNS) inflammatory alterations associated with neuropsychiatric symptoms (NPS); and to investigate their relationships with AD pathology and clinical disease progression. METHODS: We quantified a panel of 38 neuroinflammation and vascular injury markers in paired serum and cerebrospinal fluid (CSF) samples in a cohort of cognitively normal and impaired older subjects. We performed neuropsychiatric and cognitive evaluations and measured CSF biomarkers of AD pathology. Multivariate analysis determined serum and CSF neuroinflammatory alterations associated with NPS, considering cognitive status, AD pathology, and cognitive decline at follow-up visits. RESULTS: NPS were associated with distinct inflammatory profiles in serum, involving eotaxin-3, interleukin (IL)-6 and C-reactive protein (CRP); and in CSF, including soluble intracellular cell adhesion molecule-1 (sICAM-1), IL-8, 10-kDa interferon-γ-induced protein, and CRP. AD pathology interacted with CSF sICAM-1 in association with NPS. Presenting NPS was associated with subsequent cognitive decline which was mediated by CSF sICAM-1. CONCLUSIONS: Distinct systemic and CNS inflammatory processes are involved in the pathophysiology of NPS in older people. Neuroinflammation may explain the link between NPS and more rapid clinical disease progression.

Data-driven myelin water imaging based on T1 and T2 relaxometry.

Long acquisition times preclude the application of multiecho spin echo (MESE) sequences for myelin water fraction (MWF) mapping in daily clinical practice. In search of alternative methods, previous studies of interest explored the biophysical modeling of MWF from measurements of different tissue properties that can be obtained in scan times shorter than those required for the MESE. In this work, a novel data-driven estimation of MWF maps from fast relaxometry measurements is proposed and investigated. T1 and T2 relaxometry maps were acquired in a cohort of 20 healthy subjects along with a conventional MESE sequence. Whole-brain quantitative mapping was achieved with a fast protocol in 6 min 24 s. Reference MWF maps were derived from the MESE sequence (TA = 11 min 17 s) and their data-driven estimation from relaxometry measurements was investigated using three different modeling strategies: two general linear models (GLMs) with linear and quadratic regressors, respectively; a random forest regression model; and two deep neural network architectures, a U-Net and a conditional generative adversarial network (cGAN). Models were validated using a 10-fold crossvalidation. The resulting maps were visually and quantitatively compared by computing the root mean squared error (RMSE) between the estimated and reference MWF maps, the intraclass correlation coefficients (ICCs) between corresponding MWF values in different brain regions, and by performing Bland-Altman analysis. Qualitatively, the estimated maps appear to generally provide a similar, yet more blurred MWF contrast in comparison with the reference, with the cGAN model best capturing MWF variabilities in small structures. By estimating the average adjusted coefficient of determination of the GLM with quadratic regressors, we showed that 87% of the variability in the MWF values can be explained by relaxation times alone. Further quantitative analysis showed an average RMSE smaller than 0.1% for all methods. The ICC was greater than 0.81 for all methods, and the bias smaller than 2.19%. It was concluded that this work confirms the notion that relaxometry parameters contain a large part of the information on myelin water and that MWF maps can be generated from T1 /T2 data with minimal error. Among the investigated modeling approaches, the cGAN provided maps with the best trade-off between accuracy and blurriness. Fast relaxometry, like the 6 min 24 s whole-brain protocol used in this work in conjunction with machine learning, may thus have the potential to replace time-consuming MESE acquisitions.

Comparing methods of detecting and segmenting unruptured intracranial aneurysms on TOF-MRAS: The ADAM challenge.

Accurate detection and quantification of unruptured intracranial aneurysms (UIAs) is important for rupture risk assessment and to allow an informed treatment decision to be made. Currently, 2D manual measures used to assess UIAs on Time-of-Flight magnetic resonance angiographies (TOF-MRAs) lack 3D information and there is substantial inter-observer variability for both aneurysm detection and assessment of aneurysm size and growth. 3D measures could be helpful to improve aneurysm detection and quantification but are time-consuming and would therefore benefit from a reliable automatic UIA detection and segmentation method. The Aneurysm Detection and segMentation (ADAM) challenge was organised in which methods for automatic UIA detection and segmentation were developed and submitted to be evaluated on a diverse clinical TOF-MRA dataset. A training set (113 cases with a total of 129 UIAs) was released, each case including a TOF-MRA, a structural MR image (T1, T2 or FLAIR), annotation of any present UIA(s) and the centre voxel of the UIA(s). A test set of 141 cases (with 153 UIAs) was used for evaluation. Two tasks were proposed: (1) detection and (2) segmentation of UIAs on TOF-MRAs. Teams developed and submitted containerised methods to be evaluated on the test set. Task 1 was evaluated using metrics of sensitivity and false positive count. Task 2 was evaluated using dice similarity coefficient, modified hausdorff distance (95th percentile) and volumetric similarity. For each task, a ranking was made based on the average of the metrics. In total, eleven teams participated in task 1 and nine of those teams participated in task 2. Task 1 was won by a method specifically designed for the detection task (i.e. not participating in task 2). Based on segmentation metrics, the top two methods for task 2 performed statistically significantly better than all other methods. The detection performance of the top-ranking methods was comparable to visual inspection for larger aneurysms. Segmentation performance of the top ranking method, after selection of true UIAs, was similar to interobserver performance. The ADAM challenge remains open for future submissions and improved submissions, with a live leaderboard to provide benchmarking for method developments at https://adam.isi.uu.nl/.

To buy or not to buy-evaluating commercial AI solutions in radiology (the ECLAIR guidelines).

Artificial intelligence (AI) has made impressive progress over the past few years, including many applications in medical imaging. Numerous commercial solutions based on AI techniques are now available for sale, forcing radiology practices to learn how to properly assess these tools. While several guidelines describing good practices for conducting and reporting AI-based research in medicine and radiology have been published, fewer efforts have focused on recommendations addressing the key questions to consider when critically assessing AI solutions before purchase. Commercial AI solutions are typically complicated software products, for the evaluation of which many factors are to be considered. In this work, authors from academia and industry have joined efforts to propose a practical framework that will help stakeholders evaluate commercial AI solutions in radiology (the ECLAIR guidelines) and reach an informed decision. Topics to consider in the evaluation include the relevance of the solution from the point of view of each stakeholder, issues regarding performance and validation, usability and integration, regulatory and legal aspects, and financial and support services. KEY POINTS: • Numerous commercial solutions based on artificial intelligence techniques are now available for sale, and radiology practices have to learn how to properly assess these tools. • We propose a framework focusing on practical points to consider when assessing an AI solution in medical imaging, allowing all stakeholders to conduct relevant discussions with manufacturers and reach an informed decision as to whether to purchase an AI commercial solution for imaging applications. • Topics to consider in the evaluation include the relevance of the solution from the point of view of each stakeholder, issues regarding performance and validation, usability and integration, regulatory and legal aspects, and financial and support services.

CVSnet: A machine learning approach for automated central vein sign assessment in multiple sclerosis.

The central vein sign (CVS) is an efficient imaging biomarker for multiple sclerosis (MS) diagnosis, but its application in clinical routine is limited by inter-rater variability and the expenditure of time associated with manual assessment. We describe a deep learning-based prototype for automated assessment of the CVS in white matter MS lesions using data from three different imaging centers. We retrospectively analyzed data from 3 T magnetic resonance images acquired on four scanners from two different vendors, including adults with MS (n = 42), MS mimics (n = 33, encompassing 12 distinct neurological diseases mimicking MS) and uncertain diagnosis (n = 5). Brain white matter lesions were manually segmented on FLAIR* images. Perivenular assessment was performed according to consensus guidelines and used as ground truth, yielding 539 CVS-positive (CVS+ ) and 448 CVS-negative (CVS- ) lesions. A 3D convolutional neural network ("CVSnet") was designed and trained on 47 datasets, keeping 33 for testing. FLAIR* lesion patches of CVS+ /CVS- lesions were used for training and validation (n = 375/298) and for testing (n = 164/150). Performance was evaluated lesion-wise and subject-wise and compared with a state-of-the-art vesselness filtering approach through McNemar's test. The proposed CVSnet approached human performance, with lesion-wise median balanced accuracy of 81%, and subject-wise balanced accuracy of 89% on the validation set, and 91% on the test set. The process of CVS assessment, in previously manually segmented lesions, was ~ 600-fold faster using the proposed CVSnet compared with human visual assessment (test set: 4 seconds vs. 40 minutes). On the validation and test sets, the lesion-wise performance outperformed the vesselness filter method (P < 0.001). The proposed deep learning prototype shows promising performance in differentiating MS from its mimics. Our approach was evaluated using data from different hospitals, enabling larger multicenter trials to evaluate the benefit of introducing the CVS marker into MS diagnostic criteria.

Artificial Intelligence in Musculoskeletal Imaging: Review of Current Literature, Challenges, and Trends.

Artificial intelligence (AI) has gained major attention with a rapid increase in the number of published articles, mostly recently. This review provides a general understanding of how AI can or will be useful to the musculoskeletal radiologist. After a brief technical background on AI, machine learning, and deep learning, we illustrate, through examples from the musculoskeletal literature, potential AI applications in the various steps of the radiologist's workflow, from managing the request to communication of results. The implementation of AI solutions does not go without challenges and limitations. These are also discussed, as well as the trends and perspectives.

Multicontrast connectometry: a new tool to assess cerebellum alterations in early relapsing-remitting multiple sclerosis.

BACKGROUND: Cerebellar pathology occurs in late multiple sclerosis (MS) but little is known about cerebellar changes during early disease stages. In this study, we propose a new multicontrast "connectometry" approach to assess the structural and functional integrity of cerebellar networks and connectivity in early MS. METHODS: We used diffusion spectrum and resting-state functional MRI (rs-fMRI) to establish the structural and functional cerebellar connectomes in 28 early relapsing-remitting MS patients and 16 healthy controls (HC). We performed multicontrast "connectometry" by quantifying multiple MRI parameters along the structural tracts (generalized fractional anisotropy-GFA, T1/T2 relaxation times and magnetization transfer ratio) and functional connectivity measures. Subsequently, we assessed multivariate differences in local connections and network properties between MS and HC subjects; finally, we correlated detected alterations with lesion load, disease duration, and clinical scores. RESULTS: In MS patients, a subset of structural connections showed quantitative MRI changes suggesting loss of axonal microstructure and integrity (increased T1 and decreased GFA, P < 0.05). These alterations highly correlated with motor, memory and attention in patients, but were independent of cerebellar lesion load and disease duration. Neither network organization nor rs-fMRI abnormalities were observed at this early stage. CONCLUSION: Multicontrast cerebellar connectometry revealed subtle cerebellar alterations in MS patients, which were independent of conventional disease markers and highly correlated with patient function. Future work should assess the prognostic value of the observed damage.

Neural Correlate of Anterograde Amnesia in Wernicke-Korsakoff Syndrome.

The neural correlate of anterograde amnesia in Wernicke-Korsakoff syndrome (WKS) is still debated. While the capacity to learn new information has been associated with integrity of the medial temporal lobe (MTL), previous studies indicated that the WKS is associated with diencephalic lesions, mainly in the mammillary bodies and anterior or dorsomedial thalamic nuclei. The present study tested the hypothesis that amnesia in WKS is associated with a disrupted neural circuit between diencephalic and hippocampal structures. High-density evoked potentials were recorded in four severely amnesic patients with chronic WKS, in five patients with chronic alcoholism without WKS, and in ten age matched controls. Participants performed a continuous recognition task of pictures previously shown to induce a left medial temporal lobe dependent positive potential between 250 and 350 ms. In addition, the integrity of the fornix was assessed using diffusion tensor imaging (DTI). WKS, but not alcoholic patients without WKS, showed absence of the early, left MTL dependent positive potential following immediate picture repetitions. DTI indicated disruption of the fornix, which connects diencephalic and hippocampal structures. The findings support an interpretation of anterograde amnesia in WKS as a consequence of a disconnection between diencephalic and MTL structures with deficient contribution of the MTL to rapid consolidation.

Head motion parameters in fMRI differ between patients with mild cognitive impairment and Alzheimer disease versus elderly control subjects.

Motion artifacts are a well-known and frequent limitation during neuroimaging workup of cognitive decline. While head motion typically deteriorates image quality, we test the hypothesis that head motion differs systematically between healthy controls (HC), amnestic mild cognitive impairment (aMCI) and Alzheimer disease (AD) and consequently might contain diagnostic information. This prospective study was approved by the local ethics committee and includes 28 HC (age 71.0 ± 6.9 years, 18 females), 15 aMCI (age 67.7 ± 10.9 years, 9 females) and 20 AD (age 73.4 ± 6.8 years, 10 females). Functional magnetic resonance imaging (fMRI) at 3T included a 9 min echo-planar imaging sequence with 180 repetitions. Cumulative average head rotation and translation was estimated based on standard fMRI preprocessing and compared between groups using receiver operating characteristic statistics. Global cumulative head rotation discriminated aMCI from controls [p < 0.01, area under curve (AUC) 0.74] and AD from controls (p < 0.01, AUC 0.73). The ratio of rotation z versus y discriminated AD from controls (p < 0.05, AUC 0.71) and AD from aMCI (p < 0.05, AUC of 0.75). Head motion systematically differs between aMCI/AD and controls. Since motion is not random but convoluted with diagnosis, the higher amount of motion in aMCI and AD as compared to controls might be a potential confounding factor for fMRI group comparisons. Additionally, head motion not only deteriorates image quality, yet also contains useful discriminatory information and is available for free as a "side product" of fMRI data preprocessing.

Identifying 22q11.2 deletion syndrome and psychosis using resting-state connectivity patterns.

The clinical picture associated with 22q11.2 deletion syndrome (22q11DS) includes mild mental retardation and an increased risk of schizophrenia. While the clinical phenotype has been related to structural brain network alterations, there is only scarce information about functional connectivity in 22q11DS. However, such studies could lead to a better comprehension of the disease and reveal potential biomarkers for psychosis. A connectivity decoding approach was used to discriminate between 42 patients with 22q11DS and 41 controls using resting-state connectivity. The same method was then applied within the 22q11DS group to identify brain connectivity patterns specifically related to the presence of psychotic symptoms. An accuracy of 84 % was achieved in differentiating patients with 22q11DS from controls. The discriminative connections were widespread, but predominantly located in the bilateral frontal and right temporal lobes, and were significantly correlated to IQ. An 88 % accuracy was obtained for identification of existing psychotic symptoms within the patients group. The regions containing most discriminative connections included the anterior cingulate cortex (ACC), the left superior temporal and the right inferior frontal gyri. Functional connectivity alterations in 22q11DS affect mostly frontal and right temporal lobes and are related to the syndrome's mild mental retardation. These results also provide evidence that resting-state connectivity can potentially become a biomarker for psychosis and that ACC plays an important role in the development of psychotic symptoms.

Systematic Estimation of Treatment Effect on Hospitalization Risk as a Drug Repurposing Screening Method.

Drug repurposing (DR) intends to identify new uses for approved medications outside their original indication. Computational methods for finding DR candidates usually rely on prior biological and chemical information on a specific drug or target but rarely utilize real-world observations. In this work, we propose a simple and effective systematic screening approach to measure medication impact on hospitalization risk based on large-scale observational data. We use common classification systems to group drugs and diseases into broader functional categories and test for non-zero effects in each drug-disease category pair. Treatment effects on the hospitalization risk of an individual disease are obtained by combining widely used methods for causal inference and time-to-event modelling. 6468 drug-disease pairs were tested using data from the UK Biobank, focusing on cardiovascular, metabolic, and respiratory diseases. We determined key parameters to reduce the number of spurious correlations and identified 7 statistically significant associations of reduced hospitalization risk after correcting for multiple testing. Some of these associations were already reported in other studies, including new potential applications for cardioselective beta-blockers and thiazides. We also found evidence for proton pump inhibitor side effects and multiple possible associations for anti-diabetic drugs. Our work demonstrates the applicability of the present screening approach and the utility of real-world data for identifying potential DR candidates.

Tract-wise microstructural analysis informs on current and future disability in early multiple sclerosis.

OBJECTIVES: Microstructural characterization of patients with multiple sclerosis (MS) has been shown to correlate better with disability compared to conventional radiological biomarkers. Quantitative MRI provides effective means to characterize microstructural brain tissue changes both in lesions and normal-appearing brain tissue. However, the impact of the location of microstructural alterations in terms of neuronal pathways has not been thoroughly explored so far. Here, we study the extent and the location of tissue changes probed using quantitative MRI along white matter (WM) tracts extracted from a connectivity atlas. METHODS: We quantified voxel-wise T1 tissue alterations compared to normative values in a cohort of 99 MS patients. For each WM tract, we extracted metrics reflecting tissue alterations both in lesions and normal-appearing WM and correlated these with cross-sectional disability and disability evolution after 2 years. RESULTS: In early MS patients, T1 alterations in normal-appearing WM correlated better with disability evolution compared to cross-sectional disability. Further, the presence of lesions in supratentorial tracts was more strongly associated with cross-sectional disability, while microstructural alterations in infratentorial pathways yielded higher correlations with disability evolution. In progressive patients, all major WM pathways contributed similarly to explaining disability, and correlations with disability evolution were generally poor. CONCLUSIONS: We showed that microstructural changes evaluated in specific WM pathways contribute to explaining future disability in early MS, hence highlighting the potential of tract-wise analyses in monitoring disease progression. Further, the proposed technique allows to estimate WM tract-specific microstructural characteristics in clinically compatible acquisition times, without the need for advanced diffusion imaging.

Microstructural characterization of multiple sclerosis lesion phenotypes using multiparametric longitudinal analysis.

BACKGROUND AND OBJECTIVES: In multiple sclerosis (MS), slowly expanding lesions were shown to be associated with worse disability and prognosis. Their timely detection from cross-sectional data at early disease stages could be clinically relevant to inform treatment planning. Here, we propose to use multiparametric, quantitative MRI to allow a better cross-sectional characterization of lesions with different longitudinal phenotypes. METHODS: We analysed T1 and T2 relaxometry maps from a longitudinal cohort of MS patients. Lesions were classified as enlarging, shrinking, new or stable based on their longitudinal volumetric change using a newly developed automated technique. Voxelwise deviations were computed as z-scores by comparing individual patient data to T1, T2 and T2/T1 normative values from healthy subjects. We studied the distribution of microstructural properties inside lesions and within perilesional tissue. RESULTS AND CONCLUSIONS: Stable lesions exhibited the highest T1 and T2 z-scores in lesion tissue, while the lowest values were observed for new lesions. Shrinking lesions presented the highest T1 z-scores in the first perilesional ring while enlarging lesions showed the highest T2 z-scores in the same region. Finally, a classification model was trained to predict the longitudinal lesion type based on microstructural metrics and feature importance was assessed. Z-scores estimated in lesion and perilesional tissue from T1, T2 and T2/T1 quantitative maps carry discriminative and complementary information to classify longitudinal lesion phenotypes, hence suggesting that multiparametric MRI approaches are essential for a better understanding of the pathophysiological mechanisms underlying disease activity in MS lesions.

Principal components of functional connectivity: a new approach to study dynamic brain connectivity during rest.

Functional connectivity (FC) as measured by correlation between fMRI BOLD time courses of distinct brain regions has revealed meaningful organization of spontaneous fluctuations in the resting brain. However, an increasing amount of evidence points to non-stationarity of FC; i.e., FC dynamically changes over time reflecting additional and rich information about brain organization, but representing new challenges for analysis and interpretation. Here, we propose a data-driven approach based on principal component analysis (PCA) to reveal hidden patterns of coherent FC dynamics across multiple subjects. We demonstrate the feasibility and relevance of this new approach by examining the differences in dynamic FC between 13 healthy control subjects and 15 minimally disabled relapse-remitting multiple sclerosis patients. We estimated whole-brain dynamic FC of regionally-averaged BOLD activity using sliding time windows. We then used PCA to identify FC patterns, termed "eigenconnectivities", that reflect meaningful patterns in FC fluctuations. We then assessed the contributions of these patterns to the dynamic FC at any given time point and identified a network of connections centered on the default-mode network with altered contribution in patients. Our results complement traditional stationary analyses, and reveal novel insights into brain connectivity dynamics and their modulation in a neurodegenerative disease.

Altered cortical and subcortical local coherence in obstructive sleep apnea: a functional magnetic resonance imaging study.

Obstructive sleep apnea (OSA) syndrome is the most common sleep-related breathing disorder, characterized by excessive snoring and repetitive apneas and arousals, which leads to fragmented sleep and, most importantly, to intermittent nocturnal hypoxaemia during apneas. Considering previous studies about morphovolumetric alterations in sleep apnea, in this study we aimed to investigate for the first time the functional connectivity profile of OSA patients and age-gender-matched healthy controls, using resting-state functional magnetic resonance imaging (fMRI). Twenty severe OSA patients (mean age 43.2 ± 8 years; mean apnea-hypopnea index, 36.3 h(-1) ) and 20 non-apneic age-gender-body mass index (BMI)-matched controls underwent fMRI and polysomnographic (PSG) registration, as well as mood and sleepiness evaluation. Cerebro-cerebellar regional homogeneity (ReHo) values were calculated from fMRI acquisition, in order to identify pathology-related alterations in the local coherence of low-frequency signal (<0.1 Hz). Multivariate pattern classification was also performed using ReHo values as features. We found a significant pattern of cortical and subcortical abnormal local connectivity in OSA patients, suggesting an overall rearrangement of hemispheric connectivity balance, with a decrease of local coherence observed in right temporal, parietal and frontal lobe regions. Moreover, an increase in bilateral thalamic and somatosensory/motor cortices coherence have been found, a finding due possibly to an aberrant adaptation to incomplete sleep-wake transitions during nocturnal apneic episodes, induced by repetitive choke sensation and physical efforts attempting to restore breathing. Different hemispheric roles into sleep processes and a possible thalamus key role in OSA neurophysiopathology are intriguing issues that future studies should attempt to clarify.

Attention-related potentials allow for a highly accurate discrimination of mild cognitive impairment subtypes.

The three most frequent forms of mild cognitive impairment (MCI) are single-domain amnestic MCI (sd-aMCI), single-domain dysexecutive MCI (sd-dMCI) and multiple-domain amnestic MCI (md-aMCI). Brain imaging differences among single domain subgroups of MCI were recently reported supporting the idea that electroencephalography (EEG) functional hallmarks can be used to differentiate these subgroups. We performed event-related potential (ERP) measures and independent component analysis in 18 sd-aMCI, 13 sd-dMCI and 35 md-aMCI cases during the successful performance of the Attentional Network Test. Sensitivity and specificity analyses of ERP for the discrimination of MCI subgroups were also made. In center-cue and spatial-cue warning stimuli, contingent negative variation (CNV) was elicited in all MCI subgroups. Two independent components (ICA1 and 2) were superimposed in the time range on the CNV. The ICA2 was strongly reduced in sd-dMCI compared to sd-aMCI and md-aMCI (4.3 vs. 7.5% and 10.9% of the CNV component). The parietal P300 ERP latency increased significantly in sd-dMCI compared to md-aMCI and sd-aMCI for both congruent and incongruent conditions. This latency for incongruent targets allowed for a highly accurate separation of sd-dMCI from both sd-aMCI and md-aMCI with correct classification rates of 90 and 81%, respectively. This EEG parameter alone performed much better than neuropsychological testing in distinguishing sd-dMCI from md-aMCI. Our data reveal qualitative changes in the composition of the neural generators of CNV in sd-dMCI. In addition, they document an increased latency of the executive P300 component that may represent a highly accurate hallmark for the discrimination of this MCI subgroup in routine clinical settings.