Strategic white matter hyperintensity locations associated with post-stroke cognitive impairment: a multicenter study in 1568 stroke patients.

BACKGROUND: Post-stroke cognitive impairment (PSCI) occurs in up to 50% of stroke survivors. Presence of pre-existing vascular brain injury, in particular the extent of white matter hyperintensities (WMH), is associated with worse cognitive outcome after stroke, but the role of WMH location in this association is unclear. AIM: We determined if WMH in strategic white matter tracts explain cognitive performance after stroke. METHODS: Individual patient data from 9 ischemic stroke cohorts with MRI were harmonized through the Meta VCI Map consortium. The association between WMH volumes in strategic tracts and domain-specific cognitive functioning (attention and executive functioning, information processing speed, language and verbal memory) was assessed using linear mixed models and lasso regression. We used a hypothesis-driven design, primarily addressing four white matter tracts known to be strategic in memory clinic patients: the left and right anterior thalamic radiation, forceps major and left inferior fronto-occipital fasciculus. RESULTS: The total study sample consisted of 1568 patients (39.9% female, mean age: 67.3 years). Total WMH volume was strongly related to cognitive performance on all four cognitive domains. WMH volume in the left anterior thalamic radiation was significantly associated with cognitive performance on attention and executive functioning and information processing speed, and WMH volume in the forceps major with information processing speed. The multivariable lasso regression showed that these associations were independent of age, sex, education, and total infarct volume and had larger coefficients than total WMH volume. CONCLUSIONS: These results show tract-specific relations between WMH volume and cognitive performance after ischemic stroke, independent of total WMH volume. This implies that the concept of strategic lesions in PSCI extends beyond acute infarcts and also involves pre-existing WMH. DATA AVAILABILITY: The Meta VCI Map consortium is dedicated to data sharing, following our guidelines.

Multifractal Analysis in Neuroimaging.

The characteristics of biomedical signals are not captured by conventional measures like the average amplitude of the signal. The methodologies derived from fractal geometry have been a very useful approach to study the degree of irregularity of a signal. The monofractal analysis of a signal is defined by a single power-law exponent in assuming a scale invariance in time and space. However, temporal and spatial variation in the scale-invariant structure of the biomedical signal often appears. In this case, multifractal analysis is well-suited because it is defined by a multifractal spectrum of power-law exponents. There are several approaches to the implementation of this analysis, and there are numerous ways to present these.In this chapter, we review the use of multifractal analysis for the purpose of characterizing signals in neuroimaging. After describing the tenets of multifractal analysis, we present several approaches to estimating the multifractal spectrum. Finally, we describe the applications of this spectrum on biomedical signals in the characterization of several diseases in neurosciences.

Clinical and neuroimaging predictors of benzodiazepine response in catatonia: A machine learning approach.

Catatonia is a well characterized psychomotor syndrome combining motor, behavioural and neurovegetative signs. Benzodiazepines are the first-choice treatment, effective in 70 % of cases. Currently, the factors associated with benzodiazepine resistance remain unknown. We aimed to develop machine learning models using clinical and neuroimaging data to predict benzodiazepine response in catatonic patients. This study examined a cohort of catatonic patients who underwent standardized clinical evaluation, 3 T brain MRI, and benzodiazepine trial. Based on clinical response, patients were classified as benzodiazepine responders or non-responders. Cortical thickness and regional brain volumes were measured. Two machine learning models (linear model and gradient boosting tree model) were developed to identify predictors of treatment response using clinical, demographic, and neuroimaging data. The cohort included 65 catatonic patients, comprising 30 benzodiazepine responders and 35 non-responders. Using clinical data alone, the linear model achieved 63% precision, 51% recall, a specificity of 61%, and 58% AUC, while the gradient boosting tree (GBT) model attained 46% precision, 60% recall, a specificity of 62% and 64% AUC. Incorporating neuroimaging data improved model performance, with the linear model achieving 66% precision, 57% recall, a specificity of 67%, and 70% AUC, and the GBT model attaining 50% precision, 50% recall, a specificity of 62% and 70% AUC. The integration of imaging data with demographic and clinical information significantly enhanced the predictive performance of the models. The duration of the catatonic syndrome, along with the presence of mitgehen (passive obedience) and immobility/stupor, and the volume of the right medial orbito-frontal cortex emerged as important factors in predicting non-response to benzodiazepines.

A 3D convolutional neural network to classify subjects as Alzheimer's disease, frontotemporal dementia or healthy controls using brain 18F-FDG PET.

With the arrival of disease-modifying drugs, neurodegenerative diseases will require an accurate diagnosis for optimal treatment. Convolutional neural networks are powerful deep learning techniques that can provide great help to physicians in image analysis. The purpose of this study is to introduce and validate a 3D neural network for classification of Alzheimer's disease (AD), frontotemporal dementia (FTD) or cognitively normal (CN) subjects based on brain glucose metabolism. Retrospective [18F]-FDG-PET scans of 199 CE, 192 FTD and 200 CN subjects were collected from our local database, Alzheimer's disease and frontotemporal lobar degeneration neuroimaging initiatives. Training and test sets were created using randomization on a 90 %-10 % basis, and training of a 3D VGG16-like neural network was performed using data augmentation and cross-validation. Performance was compared to clinical interpretation by three specialists in the independent test set. Regions determining classification were identified in an occlusion experiment and Gradient-weighted Class Activation Mapping. Test set subjects were age- and sex-matched across categories. The model achieved an overall 89.8 % accuracy in predicting the class of test scans. Areas under the ROC curves were 93.3 % for AD, 95.3 % for FTD, and 99.9 % for CN. The physicians' consensus showed a 69.5 % accuracy, and there was substantial agreement between them (kappa = 0.61, 95 % CI: 0.49-0.73). To our knowledge, this is the first study to introduce a deep learning model able to discriminate AD and FTD based on [18F]-FDG PET scans, and to isolate CN subjects with excellent accuracy. These initial results are promising and hint at the potential for generalization to data from other centers.

A three-dimensional deep learning model for inter-site harmonization of structural MR images of the brain: Extensive validation with a multicenter dataset.

In multicenter MRI studies, pooling the imaging data can introduce site-related variabilities and can therefore bias the subsequent analyses. To harmonize the intensity distributions of brain MR images in a multicenter dataset, unsupervised deep learning methods can be employed. Here, we developed a model based on cycle-consistent adversarial networks for the harmonization of T1-weighted brain MR images. In contrast to previous works, it was designed to process three-dimensional whole-brain images in a stable manner while optimizing computation resources. Using six different MRI datasets for healthy adults (n=1525 in total) with different acquisition parameters, we tested the model in (i) three pairwise harmonizations with site effects of various sizes, (ii) an overall harmonization of the six datasets with different age distributions, and (iii) a traveling-subject dataset. Our results for intensity distributions, brain volumes, image quality metrics and radiomic features indicated that the MRI characteristics at the various sites had been effectively homogenized. Next, brain age prediction experiments and the observed correlation between the gray-matter volume and age showed that thanks to an appropriate training strategy and despite biological differences between the dataset populations, the model reinforced biological patterns. Furthermore, radiologic analyses of the harmonized images attested to the conservation of the radiologic information in the original images. The robustness of the harmonization model (as judged with various datasets and metrics) demonstrates its potential for application in retrospective multicenter studies.

MRI BrainAGE demonstrates increased brain aging in systemic lupus erythematosus patients.

Introduction: Systemic lupus erythematosus (SLE) is an autoimmune connective tissue disease affecting multiple organs in the human body, including the central nervous system. Recently, an artificial intelligence method called BrainAGE (Brain Age Gap Estimation), defined as predicted age minus chronological age, has been developed to measure the deviation of brain aging from a healthy population using MRI. Our aim was to evaluate brain aging in SLE patients using a deep-learning BrainAGE model. Methods: Seventy female patients with a clinical diagnosis of SLE and 24 healthy age-matched control females, were included in this post-hoc analysis of prospectively acquired data. All subjects had previously undergone a 3 T MRI acquisition, a neuropsychological evaluation and a measurement of neurofilament light protein in plasma (NfL). A BrainAGE model with a 3D convolutional neural network architecture, pre-trained on the 3D-T1 images of 1,295 healthy female subjects to predict their chronological age, was applied on the images of SLE patients and controls in order to compute the BrainAGE. SLE patients were divided into 2 groups according to the BrainAGE distribution (high vs. low BrainAGE). Results: BrainAGE z-score was significantly higher in SLE patients than in controls (+0.6 [±1.1] vs. 0 [±1.0], p = 0.02). In SLE patients, high BrainAGE was associated with longer reaction times (p = 0.02), lower psychomotor speed (p = 0.001) and cognitive flexibility (p = 0.04), as well as with higher NfL after adjusting for age (p = 0.001). Conclusion: Using a deep-learning BrainAGE model, we provide evidence of increased brain aging in SLE patients, which reflected neuronal damage and cognitive impairment.

White Matter Hyperintensity Volume and Poststroke Cognition: An Individual Patient Data Pooled Analysis of 9 Ischemic Stroke Cohort Studies.

BACKGROUND: White matter hyperintensities (WMH) are associated with cognitive dysfunction after ischemic stroke. Yet, uncertainty remains about affected domains, the role of other preexisting brain injury, and infarct types in the relation between WMH burden and poststroke cognition. We aimed to disentangle these factors in a large sample of patients with ischemic stroke from different cohorts. METHODS: We pooled and harmonized individual patient data (n=1568) from 9 cohorts, through the Meta VCI Map consortium (www.metavcimap.org). Included cohorts comprised patients with available magnetic resonance imaging and multidomain cognitive assessment <15 months poststroke. In this individual patient data meta-analysis, linear mixed models were used to determine the association between WMH volume and domain-specific cognitive functioning (Z scores; attention and executive functioning, processing speed, language and verbal memory) for the total sample and stratified by infarct type. Preexisting brain injury was accounted for in the multivariable models and all analyses were corrected for the study site as a random effect. RESULTS: In the total sample (67 years [SD, 11.5], 40% female), we found a dose-dependent inverse relationship between WMH volume and poststroke cognitive functioning across all 4 cognitive domains (coefficients ranging from -0.09 [SE, 0.04, P=0.01] for verbal memory to -0.19 [SE, 0.03, P<0.001] for attention and executive functioning). This relation was independent of acute infarct volume and the presence of lacunes and old infarcts. In stratified analyses, the relation between WMH volume and domain-specific functioning was also largely independent of infarct type. CONCLUSIONS: In patients with ischemic stroke, increasing WMH volume is independently associated with worse cognitive functioning across all major domains, regardless of old ischemic lesions and infarct type.

Anxiety in Parkinson's Disease Is Associated with Changes in Brain Structural Connectivity.

BACKGROUND: Anxiety in Parkinson's disease (PD) has been associated with grey matter changes and functional changes in anxiety-related neuronal circuits. So far, no study has analyzed white matter (WM) changes in patients with PD and anxiety. OBJECTIVE: The aim of this study was to identify WM changes by comparing PD patients with and without anxiety, using diffusion-tensor imaging (DTI). METHODS: 108 non-demented PD patients with (n = 31) and without (n = 77) anxiety as defined by their score on the Parkinson Anxiety Scale participated. DTI was used to determine the fractional anisotropy (FA) and mean diffusivity (MD) in specific tracts within anxiety-related neuronal circuits. Mean FA and MD were compared between groups and correlated with the severity of anxiety adjusted by sex, center, Hoehn & Yahr stage, levodopa equivalent daily dosage, and Hamilton depression rating scale. RESULTS: Compared to patients without anxiety, PD patients with anxiety showed lower FA within the striato-orbitofrontal, striato-cingulate, cingulate-limbic, and caudate-thalamic tracts; higher FA within the striato-limbic and accumbens-thalamic tracts; higher MD within the striato-thalamic tract and lower MD within the striato-limbic tract. CONCLUSIONS: Anxiety in PD is associated with microstructural alterations in anxiety-related neuronal circuits within the WM. This result reinforces the view that PD-related anxiety is linked to structural alteration within the anxiety-related brain circuits.

Hypothalamic neuroglial plasticity is regulated by anti-Müllerian hormone and disrupted in polycystic ovary syndrome.

BACKGROUND: Polycystic ovary syndrome (PCOS) is the most common reproductive-endocrine disorder affecting between 5 and 18% of women worldwide. An elevated frequency of pulsatile luteinizing hormone (LH) secretion and higher serum levels of anti-Müllerian hormone (AMH) are frequently observed in women with PCOS. The origin of these abnormalities is, however, not well understood. METHODS: We studied brain structure and function in women with and without PCOS using proton magnetic resonance spectroscopy (MRS) and diffusion tensor imaging combined with fiber tractography. Then, using a mouse model of PCOS, we investigated by electron microscopy whether AMH played a role on the regulation of hypothalamic structural plasticity. FINDINGS: Increased AMH serum levels are associated with increased hypothalamic activity/axonal-glial signalling in PCOS patients. Furthermore, we demonstrate that AMH promotes profound micro-structural changes in the murine hypothalamic median eminence (ME), creating a permissive environment for GnRH secretion. These include the retraction of the processes of specialized AMH-sensitive ependymo-glial cells called tanycytes, allowing more GnRH neuron terminals to approach ME blood capillaries both during the run-up to ovulation and in a mouse model of PCOS. INTERPRETATION: We uncovered a central function for AMH in the regulation of fertility by remodeling GnRH terminals and their tanycytic sheaths, and provided insights into the pivotal role of the brain in the establishment and maintenance of neuroendocrine dysfunction in PCOS. FUNDING: INSERM (U1172), European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement n° 725149), CHU de Lille, France (Bonus H).

Cognitive Behavioral Therapy for Anxiety in Parkinson's Disease Induces Functional Brain Changes.

BACKGROUND: Cognitive behavioral therapy (CBT) reduces anxiety symptoms in patients with Parkinson's disease (PD). OBJECTIVE: The objective of this study was to identify changes in functional connectivity in the brain after CBT for anxiety in patients with PD. METHODS: Thirty-five patients with PD and clinically significant anxiety were randomized over two groups: CBT plus clinical monitoring (10 CBT sessions) or clinical monitoring only (CMO). Changes in severity of anxiety symptoms were assessed with the Parkinson Anxiety Scale (PAS). Resting-state functional brain MRI was performed at baseline and after the intervention. Functional networks were extracted by an Independent Component Analysis (ICA). Functional connectivity (FC) changes between structures involved in the PD-related anxiety circuits, such as the fear circuit (involving limbic, frontal, and cingulate structures) and the cortico-striato-thalamo-cortical limbic circuit, and both within and between functional networks were compared between groups and regressed with anxiety symptoms changes. RESULTS: Compared to CMO, CBT reduced the FC between the right thalamus and the bilateral orbitofrontal cortices and increased the striato-frontal FC. CBT also increased the fronto-parietal FC within the central executive network (CEN) and between the CEN and the salience network. After CBT, improvement of PAS-score was associated with an increased striato-cingulate and parieto-temporal FC, and a decreased FC within the default-mode network and between the dorsal attentional network and the language network. CONCLUSION: CBT in PD-patients improves anxiety symptoms and is associated with functional changes reversing the imbalance between PD-related anxiety circuits and reinforcing cognitive control on emotional processing.

Radiomics-Derived Brain Age Predicts Functional Outcome After Acute Ischemic Stroke.

BACKGROUND AND OBJECTIVES: While chronological age is one of the most influential determinants of poststroke outcomes, little is known of the impact of neuroimaging-derived biological "brain age." We hypothesized that radiomics analyses of T2-FLAIR images texture would provide brain age estimates and that advanced brain age of patients with stroke will be associated with cardiovascular risk factors and worse functional outcomes. METHODS: We extracted radiomics from T2-FLAIR images acquired during acute stroke clinical evaluation. Brain age was determined from brain parenchyma radiomics using an ElasticNet linear regression model. Subsequently, relative brain age (RBA), which expresses brain age in comparison with chronological age-matched peers, was estimated. Finally, we built a linear regression model of RBA using clinical cardiovascular characteristics as inputs and a logistic regression model of favorable functional outcomes taking RBA as input. RESULTS: We reviewed 4,163 patients from a large multisite ischemic stroke cohort (mean age = 62.8 years, 42.0% female patients). T2-FLAIR radiomics predicted chronological ages (mean absolute error = 6.9 years, r = 0.81). After adjustment for covariates, RBA was higher and therefore described older-appearing brains in patients with hypertension, diabetes mellitus, a history of smoking, and a history of a prior stroke. In multivariate analyses, age, RBA, NIHSS, and a history of prior stroke were all significantly associated with functional outcome (respective adjusted odds ratios: 0.58, 0.76, 0.48, 0.55; all p-values < 0.001). Moreover, the negative effect of RBA on outcome was especially pronounced in minor strokes. DISCUSSION: T2-FLAIR radiomics can be used to predict brain age and derive RBA. Older-appearing brains, characterized by a higher RBA, reflect cardiovascular risk factor accumulation and are linked to worse outcomes after stroke.

Quantitative susceptibility mapping demonstrates different patterns of iron overload in subtypes of early-onset Alzheimer's disease.

OBJECTIVES: We aimed to define brain iron distribution patterns in subtypes of early-onset Alzheimer's disease (EOAD) by the use of quantitative susceptibility mapping (QSM). METHODS: EOAD patients prospectively underwent MRI on a 3-T scanner and concomitant clinical and neuropsychological evaluation, between 2016 and 2019. An age-matched control group was constituted of cognitively healthy participants at risk of developing AD. Volumetry of the hippocampus and cerebral cortex was performed on 3DT1 images. EOAD subtypes were defined according to the hippocampal to cortical volume ratio (HV:CTV). Limbic-predominant atrophy (LPMRI) is referred to HV:CTV ratios below the 25th percentile, hippocampal-sparing (HpSpMRI) above the 75th percentile, and typical-AD between the 25th and 75th percentile. Brain iron was estimated using QSM. QSM analyses were made voxel-wise and in 7 regions of interest within deep gray nuclei and limbic structures. Iron distribution in EOAD subtypes and controls was compared using an ANOVA. RESULTS: Sixty-eight EOAD patients and 43 controls were evaluated. QSM values were significantly higher in deep gray nuclei (p < 0.001) and limbic structures (p = 0.04) of EOAD patients compared to controls. Among EOAD subtypes, HpSpMRI had the highest QSM values in deep gray nuclei (p < 0.001) whereas the highest QSM values in limbic structures were observed in LPMRI (p = 0.005). QSM in deep gray nuclei had an AUC = 0.92 in discriminating HpSpMRI and controls. CONCLUSIONS: In early-onset Alzheimer's disease patients, we observed significant variations of iron distribution reflecting the pattern of brain atrophy. Iron overload in deep gray nuclei could help to identify patients with atypical presentation of Alzheimer's disease. KEY POINTS: • In early-onset AD patients, QSM indicated a significant brain iron overload in comparison with age-matched controls. • Iron load in limbic structures was higher in participants with limbic-predominant subtype. • Iron load in deep nuclei was more important in participants with hippocampal-sparing subtype.

Relation between retina, cognition and brain volumes in MS: a consequence of asymptomatic optic nerve lesions.

INTRODUCTION: Asymptomatic optic nerve lesions are frequent in multiple sclerosis (MS) and their impact on cognition and/or brain volume has never been taken into account. PATIENTS AND METHODS: We used the data from the cross-sectional Visual Ways in MS (VWIMS) study including relapsing remitting MS. All patients underwent brain and optic nerve Magnetic Resonance Imaging (MRI) including Double Inversion Recuperation (DIR) sequence, retinal OCT, and cognitive evaluation with the Brief International Cognitive Assessment in MS (BICAMS). We measured the association between OCT findings (thickness/volume of retinal layers) and extra-visual parameters (cerebral volumes and BICAMS scores) in optic nerves with and/or without the presence of DIR asymptomatic optic nerve hypersignal. RESULTS: Between March and December 2017, we included 98 patients. Two patients were excluded. Over the 192 eyes, 73 had at least one clinical history of optic neuritis (ON-eyes) whereas 119 were asymptomatic (NON-eyes). Among the 119 NON-eyes, 58 had 3D-DIR optic nerve hypersignal (48.7%). We confirmed significant associations between some retinal OCT measures and some extra-visual parameters (cerebral volumes, cognitive scores) in NON-eyes. Unexpectedly, these associations were found when an asymptomatic optic nerve DIR-hypersignal was present on MRI, but not when it was absent. CONCLUSION: Our study showed a relation between OCT measures and extra-visual parameters in NON-eyes MS patients. As a confusion factor, asymptomatic optic nerve lesions may be the explanation of the relation between OCT measures and extra-visual parameters. Retinal OCT seems to be far more a "window over the optic nerve" than a "window over the brain".

Trial of Deferiprone in Parkinson's Disease.

BACKGROUND: Iron content is increased in the substantia nigra of persons with Parkinson's disease and may contribute to the pathophysiology of the disorder. Early research suggests that the iron chelator deferiprone can reduce nigrostriatal iron content in persons with Parkinson's disease, but its effects on disease progression are unclear. METHODS: We conducted a multicenter, phase 2, randomized, double-blind trial involving participants with newly diagnosed Parkinson's disease who had never received levodopa. Participants were assigned (in a 1:1 ratio) to receive oral deferiprone at a dose of 15 mg per kilogram of body weight twice daily or matched placebo for 36 weeks. Dopaminergic therapy was withheld unless deemed necessary for symptom control. The primary outcome was the change in the total score on the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS; range, 0 to 260, with higher scores indicating more severe impairment) at 36 weeks. Secondary and exploratory clinical outcomes at up to 40 weeks included measures of motor and nonmotor disability. Brain iron content measured with the use of magnetic resonance imaging was also an exploratory outcome. RESULTS: A total of 372 participants were enrolled; 186 were assigned to receive deferiprone and 186 to receive placebo. Progression of symptoms led to the initiation of dopaminergic therapy in 22.0% of the participants in the deferiprone group and 2.7% of those in the placebo group. The mean MDS-UPDRS total score at baseline was 34.3 in the deferiprone group and 33.2 in the placebo group and increased (worsened) by 15.6 points and 6.3 points, respectively (difference, 9.3 points; 95% confidence interval, 6.3 to 12.2; P<0.001). Nigrostriatal iron content decreased more in the deferiprone group than in the placebo group. The main serious adverse events with deferiprone were agranulocytosis in 2 participants and neutropenia in 3 participants. CONCLUSIONS: In participants with early Parkinson's disease who had never received levodopa and in whom treatment with dopaminergic medications was not planned, deferiprone was associated with worse scores in measures of parkinsonism than those with placebo over a period of 36 weeks. (Funded by the European Union Horizon 2020 program; FAIRPARK-II ClinicalTrials.gov number, NCT02655315.).

Brain network characteristics and cognitive performance in motor subtypes of Parkinson's disease: A resting state fMRI study.

INTRODUCTION: Parkinson's disease (PD) is a heterogeneous disorder with great variability in motor and non-motor manifestations. It is hypothesized that different motor subtypes are characterized by different neuropsychiatric and cognitive symptoms, but the underlying correlates in cerebral connectivity remain unknown. Our aim is to compare brain network connectivity between the postural instability and gait disorder (PIGD) and tremor-dominant (TD) subtypes, using both a within- and between-network analysis. METHODS: This cross-sectional resting-state fMRI study includes 81 PD patients, 54 belonging to the PIGD and 27 to the TD subgroup. Group-level spatial maps were created using independent component analysis. Differences in functional connectivity were investigated using dual regression analysis and inter-network connectivity analysis. An additional voxel-based morphometry analysis was performed to examine if results were influenced by grey matter atrophy. RESULTS: The PIGD subgroup scored worse than the TD subgroup on all cognitive domains. Resting-state fMRI network analyses suggested that the connection between the visual and sensorimotor network is a potential differentiator between PIGD and TD subgroups. However, after correcting for dopaminergic medication use these results were not significant anymore. There was no between-group difference in grey matter volume. CONCLUSION: Despite clear motor and cognitive differences between the PIGD and TD subtypes, no significant differences were found in network connectivity. Methodological challenges, substantial symptom heterogeneity and many involved variables make analyses and hypothesis building around PD subtypes highly complex. More sensitive visualisation methods combined with machine learning approaches may be required in the search for characteristic underpinnings of PD subtypes.

Can Dopamine Responsiveness Be Predicted in Parkinson's Disease Without an Acute Administration Test?

BACKGROUND: Dopamine responsiveness (dopa-sensitivity) is an important parameter in the management of patients with Parkinson's disease (PD). For quantification of this parameter, patients undergo a challenge test with acute Levodopa administration after drug withdrawal, which may lead to patient discomfort and use of significant resources. OBJECTIVE: Our objective was to develop a predictive model combining clinical scores and imaging. METHODS: 350 patients, recruited by 13 specialist French centers and considered for deep brain stimulation, underwent an acute L-dopa challenge (dopa-sensitivity > 30%), full assessment, and MRI investigations, including T1w and R2* images. Data were randomly divided into a learning base from 10 centers and data from the remaining centers for testing. A machine selection approach was applied to choose the optimal variables and these were then used in regression modeling. Complexity of the modelling was incremental, while the first model considered only clinical variables, the subsequent included imaging features. The performances were evaluated by comparing the estimated values and actual valuesResults:Whatever the model, the variables age, sex, disease duration, and motor scores were selected as contributors. The first model used them and the coefficients of determination (R2) was 0.60 for the testing set and 0.69 in the learning set (p < 0.001). The models that added imaging features enhanced the performances: with T1w (R2 = 0.65 and 0.76, p < 0.001) and with R2* (R2 = 0.60 and 0.72, p < 0.001). CONCLUSION: These results suggest that modeling is potentially a simple way to estimate dopa-sensitivity, but requires confirmation in a larger population, including patients with dopa-sensitivity < 30.

Network impact score is an independent predictor of post-stroke cognitive impairment: A multicenter cohort study in 2341 patients with acute ischemic stroke.

BACKGROUND: Post-stroke cognitive impairment (PSCI) is a common consequence of stroke. Accurate prediction of PSCI risk is challenging. The recently developed network impact score, which integrates information on infarct location and size with brain network topology, may improve PSCI risk prediction. AIMS: To determine if the network impact score is an independent predictor of PSCI, and of cognitive recovery or decline. METHODS: We pooled data from patients with acute ischemic stroke from 12 cohorts through the Meta VCI Map consortium. PSCI was defined as impairment in ≥ 1 cognitive domain on neuropsychological examination, or abnormal Montreal Cognitive Assessment. Cognitive recovery was defined as conversion from PSCI < 3 months post-stroke to no PSCI at follow-up, and cognitive decline as conversion from no PSCI to PSCI. The network impact score was related to serial measures of PSCI using Generalized Estimating Equations (GEE) models, and to PSCI stratified according to post-stroke interval (<3, 3-12, 12-24, >24 months) and cognitive recovery or decline using logistic regression. Models were adjusted for age, sex, education, prior stroke, infarct volume, and study site. RESULTS: We included 2341 patients with 4657 cognitive assessments. PSCI was present in 398/844 patients (47%) <3 months, 709/1640 (43%) at 3-12 months, 243/853 (28%) at 12-24 months, and 208/522 (40%) >24 months. Cognitive recovery occurred in 64/181 (35%) patients and cognitive decline in 26/287 (9%). The network impact score predicted PSCI in the univariable (OR 1.50, 95%CI 1.34-1.68) and multivariable (OR 1.27, 95%CI 1.10-1.46) GEE model, with similar ORs in the logistic regression models for specified post-stroke intervals. The network impact score was not associated with cognitive recovery or decline. CONCLUSIONS: The network impact score is an independent predictor of PSCI. As such, the network impact score may contribute to a more precise and individualized cognitive prognostication in patients with ischemic stroke. Future studies should address if multimodal prediction models, combining the network impact score with demographics, clinical characteristics and other advanced brain imaging biomarkers, will provide accurate individualized prediction of PSCI. A tool for calculating the network impact score is freely available at https://metavcimap.org/features/software-tools/lsm-viewer/.

Heterogeneity of PD-MCI in Candidates to Subthalamic Deep Brain Stimulation: Associated Cortical and Subcortical Modifications.

BACKGROUND: Parkinson's disease mild cognitive impairment (PD-MCI) is frequent and heterogenous. There is no consensus about its influence on subthalamic deep brain stimulation (STN-DBS) outcomes. OBJECTIVE: To determine the prevalence of PD-MCI and its subtypes in candidates to STN-DBS. Secondarily, we sought to identify MRI structural markers associated with cognitive impairment in these subgroups. METHODS: Baseline data from the French multicentric PREDISTIM cohort were used. Candidates to STN-DBS were classified according to their cognitive performance in normal cognition (PD-NC) or PD-MCI. The latter included frontostriatal (PD-FS) and posterior cortical (PD-PC) subtypes. Between-group comparisons were performed on demographical and clinical variables as well as on T1-weighted MRI sequences at the cortical and subcortical levels. RESULTS: 320 patients were included: 167 (52%) PD-NC and 153 (48%) PD-MCI patients. The latter group included 123 (80%) PD-FS and 30 (20%) PD-PC patients. There was no between-group difference regarding demographic and clinical variables. PD-PC patients had significantly lower global efficiency than PD-FS patients and significantly worse performance on visuospatial functions, episodic memory, and language. Compared to PD-NC, PD-MCI patients had cortical thinning and radiomic-based changes in the left caudate nucleus and hippocampus. There were no significant differences between the PD-MCI subtypes. CONCLUSION: Among the candidates to STN-DBS, a significant proportion has PD-MCI which is associated with cortical and subcortical alterations. Some PD-MCI patients have posterior cortical deficits, a subtype known to be at higher risk of dementia.

Neuroimaging outcomes associated with mild cognitive impairment subtypes in Parkinson's disease: A systematic review.

BACKGROUND: Mild cognitive impairment in Parkinson's disease (PD-MCI) is heterogenous and cognitive subtypes have been identified. However, the anatomo-functional bases of each subtype remain partly unknown. OBJECTIVE: To propose a description of the current literature on neuroimaging findings associated with cognitive subtypes of PD-MCI. METHODS: PubMed/Medline, Embase, PsycINFO and the Cochrane Library databases were searched (until April 2021). Studies comparing PD-MCI cognitive subtypes with healthy controls (HC) and PD patients with normal cognition (PD-NC) on any neuroimaging outcome were included. RESULTS: Ten studies met the inclusion criteria. Six used structural MRI methods, two functional MRI methods, one electroencephalography and five positron or single-photon emission tomography. Most studies (n = 8) determined PD-MCI subtypes based on memory impairment and two based on executive impairment. Compared with HC and/or PD-NC, brain modifications were found in PD patients (a) with amnestic MCI and, to a lesser extent, non-amnestic MCI in occipital, parietal and temporal regions, (b) with executive MCI in frontal and striatal regions and (c) with non-executive MCI in posterior cortical regions. CONCLUSIONS: Very few neuroimaging studies have considered cognitive heterogeneity that exists within PD-MCI, making it difficult to draw robust conclusions regarding brain modifications associated with specific subtypes. Given the promising potential of neuroimaging methods in both clinical practice and research, further studies are needed to overcome the limitations of the current literature.

Intrusive experiences in posttraumatic stress disorder: Treatment response induces changes in the directed functional connectivity of the anterior insula.

BACKGROUND: One of the core features of posttraumatic stress disorder (PTSD) is re-experiencing trauma. The anterior insula (AI) has been proposed to play a crucial role in these intrusive experiences. However, the dynamic function of the AI in re-experiencing trauma and its putative modulation by effective therapy need to be specified. METHODS: Thirty PTSD patients were enrolled and exposed to traumatic memory reactivation therapy. Resting-state functional magnetic resonance imaging (fMRI) scans were acquired before and after treatment. To explore AI-directed influences over the rest of the brain, we referred to a mixed model using pre-/posttreatment Granger causality analysis seeded on the AI as a within-subject factor and treatment response as a between-subject factor. To further identify correlates of re-experiencing trauma, we investigated how intrusive severity affected (i) causality maps and (ii) the spatial stability of other intrinsic brain networks. RESULTS: We observed changes in AI-directed functional connectivity patterns in PTSD patients. Many within- and between-network causal paths were found to be less influenced by the AI after effective therapy. Insular influences were found to be positively correlated with re-experiencing symptoms, while they were linked with a stronger default mode network (DMN) and more unstable central executive network (CEN) connectivity. CONCLUSION: We showed that directed changes in AI signaling to the DMN and CEN at rest may underlie the degree of re-experiencing symptoms in PTSD. A positive response to treatment further induced changes in network-to-network anticorrelated patterns. Such findings may guide targeted neuromodulation strategies in PTSD patients not suitably improved by conventional treatment.