Explore the vast range of titles available.

Background and Objectives: In anticoagulated atrial fibrillation (AF) patients, the validity of models recommended for the stratification of the risk ratio between benefits and hemorrhage risk is limited. Cerebral small vessel disease (SVD) represents the pathologic substrate for primary intracerebral hemorrhage and ischemic stroke. We hypothesize that biological markers—both circulating and imaging-based—and their possible interaction, might improve the prediction of bleeding risk in AF patients under treatment with any type of oral anticoagulant. Materials and Methods: The Strat-AF study is an observational, prospective, single-center hospital-based study enrolling patients with AF, aged 65 years or older, and with no contraindications to magnetic resonance imaging (MRI), referring to Center of Thrombosis outpatient clinic of our University Hospital for the management of oral anticoagulation therapy. Recruited patients are evaluated by means of a comprehensive protocol, with clinical, cerebral MRI, and circulating biomarkers assessment at baseline and after 18 months. The main outcome is SVD progression—particularly microbleeds—as a selective surrogate marker of hemorrhagic complication. Stroke occurrence (ischemic or hemorrhagic) and the progression of functional, cognitive, and motor status will be evaluated as secondary outcomes. Circulating biomarkers may further improve predictive potentials. Results: Starting from September 2017, 194 patients (mean age 78.1 ± 6.7, range 65–97; 61% males) were enrolled. The type of AF was paroxysmal in 93 patients (48%), and persistent or permanent in the remaining patients. Concerning the type of oral anticoagulant, 57 patients (29%) were on vitamin K antagonists, and 137 (71%) were on direct oral anticoagulants. Follow-up clinical evaluation and brain MRI are ongoing. Conclusions: The Strat-AF study may be an essential step towards the exploration of the role of a combined clinical biomarker or multiple biomarker models in predicting stroke risk in AF, and might sustain the incorporation of such new markers in the existing stroke prediction schemes by the demonstration of a greater incremental value in predicting stroke risk and improvement in clinical outcomes in a cost-effective fashion.

Cognitive impairment, fatigue, and depression are common in multiple sclerosis (MS), potentially due to disruption of regional functional connectivity caused by white matter (WM) lesions. We explored whether WM lesions functionally connected to specific brain regions contribute to these MS-related manifestations. A total of 596 MS patients underwent 3T brain MRI acquisition, neurologic assessment, and neuropsychological evaluation (Brief Repeatable Battery, Modified Fatigue Impact Scale [MFIS], and Montgomery-Åsberg Depression Rating Scale [MADRS]). Voxel-wise lesion probability maps were compared between subgroups based on cognition, fatigue, or depression. Lesion distributions were linked to a brain functional connectivity atlas to map lesion network associations. Lesion network maps (LNMs) were then compared among subgroups (p < 0.05, FWE-corrected). One hundred twenty-six (27.2%) MS patients were cognitively impaired and showed significantly more widespread WM lesions, more strongly functionally connected to bilateral hippocampi, thalami, cerebellum, and occipital cortices (corrected-p < 0.05) than cognitively preserved patients. Lesion networks were similar for impaired processing speed/attention. Verbal memory deficits were associated with WM lesions connected to parahippocampi, temporal pole, and cerebellum (corrected-p ≤ 0.05), while verbal fluency deficits involved connections to thalami, putamen, caudate nuclei, anterior cingulate cortex, and cerebellum (corrected-p ≤ 0.05). No significant lesion distribution or network connectivity differences were found in patients with visual memory deficits, fatigue (MFIS ≥ 38, 184/493 [37.3%]) or depression (MADRS > 9, 192/495 [38.8%]). Regional WM lesions disrupting connections to the hippocampus, thalamus, cerebellum, and temporo-occipital cortices contribute to cognitive impairment, but not fatigue or depression. LNM may clarify mechanisms underlying cognitive deficits in MS.

Objective The present study aimed to identify the clinical and MRI features of the distinct cognitive phenotypes in pediatric multiple sclerosis (pedMS). Methods PedMS patients (n = 73) and healthy controls (n = 30) underwent clinical examination and 3.0T MRI. All patients completed neuropsychological testing, and cognitive phenotypes were identified by performing K‐means clustering on cognitive scores. MRI metrics included brain T2‐hyperintese lesion volume and normalized brain volumes. Within seven cognitively relevant cortical networks, structural disconnectivity (i.e., the mean percentage of streamlines connecting each pair of cortical regions passing through a lesion) and resting‐state (RS) functional connectivity (FC) were estimated. Results Three cognitive phenotypes emerged: Preserved cognition (PC; n = 27, 37%), mild verbal learning and memory/semantic fluency involvement (MVS; n = 28, 38%), and multidomain involvement (MI; n = 18, 25%). Age, sex, and disease duration did not differ among groups. Compared with healthy subjects, PC patients had decreased RS FC within the default mode network (p = 0.045); MVS patients exhibited lower cortical volume and reduced RS FC within the frontoparietal network (all p = 0.045); and MI patients showed decreased volumes in all brain compartments except the hippocampus, and reduced RS FC within the frontoparietal network (all p ≤ 0.045). Compared to PC, MI patients had more severe disability and higher structural disconnectivity within four cortical networks (all p ≤ 0.045). Compared to PC and MVS, MI patients had lower intelligence quotient (all p ≤ 0.005). Interpretation We identified three cognitive phenotypes in pedMS that demonstrate the existence of a spectrum of impairment. Such phenotypes showed distinct clinical and MRI characteristics that contributed to explain their cognitive profiles.

ObjectiveTo investigate monoaminergic network abnormalities in patients with multiple sclerosis (MS) according to their fatigue and depressive status through a positron emission tomography (PET)-based constrained independent component analysis (ICA) on resting state (RS) functional MRI (fMRI).MethodsIn this prospective study, 213 patients with MS (mean age=40.6±12.5 years; 94/119 men/women; 153 relapsing-remitting; 60 progressive) and 62 healthy controls (HCs, mean age=39.0±10.4 years; 30/32 men/women) underwent neurological, fatigue, depression and RS fMRI assessment. Patterns of dopamine, norepinephrine-related and serotonin-related RS functional connectivity (FC) were derived by ICA, constrained to PET atlases for dopamine, norepinephrine and serotonin transporters, obtained in HCs’ brain.ResultsCompared with HCs, patients with MS showed abnormalities in all three explored monoaminergic networks, mostly with decreased RS FC within PET-guided monoaminergic networks in frontal regions and subcortical areas including the cerebellum and thalamus, and increased RS FC in temporo-parieto-occipital cortical areas, including bilateral precunei.MS-related fatigue was associated with decreased RS FC within the PET-guided dopamine network in the left thalamus and left cerebellum, and with increased RS FC within the PET-guided serotonin network in the left middle occipital gyrus. MS-related depression was associated with more distributed abnormalities involving the three explored monoaminergic networks, resulting in overall reduced RS FC in the frontal lobe, limbic areas and the precuneus.ConclusionsPatients with MS present diffuse dysregulation in the monoaminergic networks. Specific alterations in these networks were associated with fatigue and depression, providing a pathological marker for these bothersome symptoms and putative targets for their treatment.

In multiple sclerosis (MS), gray matter (GM) atrophy progresses in a non-random manner, possibly in regions with a high distribution of specific neurotransmitters involved in several relevant central nervous system functions. We investigated the associations among regional GM atrophy, atlas-based neurotransmitter distributions and clinical manifestations in a large MS patients’ group. Brain 3 T MRI scans, neurological examinations and neuropsychological evaluations were obtained from 286 MS patients and 172 healthy controls (HC). Spatial correlations among regional GM volume differences and atlas-based nuclear imaging-derived neurotransmitter maps, and their associations with MS clinical features were investigated using voxel-based morphometry and JuSpace toolbox. Compared to HC, MS patients showed widespread GM atrophy being spatially correlated with the majority of neurotransmitter maps (false discovery rate [FDR]-p ≤ 0.004). Patients with a disease duration ≥ 5 vs < 5 years had significant cortical, subcortical and cerebellar atrophy, being spatially correlated with a higher distribution of serotoninergic and dopaminergic receptors (FDR-p ≤ 0.03). Compared to mildly-disabled patients, those with Expanded Disability Status Scale ≥ 3.0 or ≥ 4.0 had significant cortical, subcortical and cerebellar atrophy being associated with serotonergic, dopaminergic, opioid and cholinergic maps (FDR-p ≤ 0.04). Cognitively impaired vs cognitively preserved patients had widespread GM atrophy being spatially associated with serotonergic, dopaminergic, noradrenergic, cholinergic and glutamatergic maps (FDR-p ≤ 0.04). Fatigued vs non-fatigued MS patients had significant cortical, subcortical and cerebellar atrophy, not associated with neurotransmitter maps. No significant association between GM atrophy and neurotransmitter maps was found for depression. Regional GM atrophy with specific neurotransmitter systems may explain part of MS clinical manifestations, including locomotor disability, cognitive impairment and fatigue.

Background Available criteria for cognitive phenotypes in multiple sclerosis (MS) do not consider the severity of impairment. Objectives To identify cognitive phenotypes with varying degrees of impairment in MS patients and describe their demographic, clinical and MRI characteristics. Methods Two hundred and forty-three MS patients and 158 healthy controls underwent neuropsychological tests to assess memory, attention, and executive function. For each domain, mild impairment was defined as performing 1.5 standard deviations below the normative mean on two tests, while the threshold for significant impairment was 2 standard deviations. Patients were classified into cognitive phenotypes based on severity of the impairment (mild/significant) and number of domains affected (one/more). Results Five cognitive phenotypes emerged: Preserved cognition (PC; 56%), Mild Single-Domain Impairment (MSD; 15%), Mild Multi-Domain Impairment (MMD; 9%), Significant Single-Domain Impairment (SSD; 12%), Significant Multi-Domain Impairment (SMD; 8%). Compared with PC, MSD patients were older, had longer disease duration (DD) and higher T2-hyperintense lesion volume (LV; all p ≤ 0.02); MMD patients were older, had longer DD, higher disability, higher T2 LV and lower thalamic volume (all p ≤ 0.01); SSD patients had longer DD and lower gray matter cortical volume, thalamic, caudate, putamen and accumbens volumes (all p ≤ 0.04); and SMD patients were older, had longer DD, higher disability and more extensive structural damage in all brain regions explored (all p ≤ 0.03), except white matter and amygdala volumes. Conclusions We identified five cognitive phenotypes with graded levels of impairment. These phenotypes were characterized by distinct demographic, clinical and MRI features, indicating potential variations in the neural substrates of dysfunction throughout disease stages.

Background Monoaminergic network dysfunction is thought to underpin depression in multiple sclerosis (MS) patients. However, longitudinal studies are lacking. Objectives Here, we investigated the association between development of depressive symptoms in MS and changes of resting-state functional connectivity (RS FC) within monoaminergic networks. Methods Forty-nine MS patients without depression [Montgomery–Asberg Depression Scale (MADRS) ≤ 9] and 27 healthy controls underwent clinical and 3.0 T RS FC assessment at baseline and after a median follow-up of 1.6 years (interquartile range 1.0–2.1 years). Monoamine-related RS FC was derived by independent component analysis, constrained to PET atlases for dopamine, noradrenaline and serotonin transporters. Longitudinal changes of RS FC within monoaminergic networks and their correlations with MADRS scores were assessed. Results At baseline, MS patients showed decreased RS FC vs healthy controls in all PET-guided monoaminergic networks in frontal, cingulate and cerebellar cortices, and increased RS FC in parieto-occipital regions. Fourteen (29%) MS patients developed depressive symptoms (MADRS > 9) at follow-up (D-MS) and exhibited widespread RS FC decrease over time in the PET-guided dopamine network, mainly in orbitofrontal, occipital, anterior cingulate and precuneal cortices compared to patients who did not develop depressive symptoms. In D-MS, decreased RS FC over time was also observed in parahippocampal and occipital regions of the PET-guided noradrenaline network. Decreased RS FC over time in dopamine and noradrenaline PET-guided networks correlated with concomitant increased MADRS scores ( r  = range − 0.65/− 0.61, p  < 0.001). Conclusions The development of depressive symptoms in MS patients was associated with specific RS FC changes within the dopamine and noradrenaline networks.

Background Fatigue is commonly observed in pediatric multiple sclerosis (pedMS) patients, but its underlying mechanisms remain largely unexplored. We evaluated whether resting-state (RS) functional connectivity (FC) abnormalities in monoaminergic networks contributed to explain fatigue in pedMS. Methods Fifty-five pedMS and twenty-three matched healthy controls (HC) underwent clinical and RS functional MRI assessment. Patients with Fatigue Severity Scale (FSS) score ≥ 4 were classified as fatigued (F). Patterns of dopamine-, noradrenaline- and serotonin-related RS FC were derived by constrained independent component analysis, using PET atlases for dopamine, noradrenaline, and serotonin transporters obtained in HCs’ brain. Results Compared to non-fatigued (NF)-pedMS patients and HC, F-pedMS patients (15/55, 27.3%) showed decreased dopamine-related RS FC in the right postcentral gyrus. They also showed decreased dopamine-related RS FC in the left insula vs. HC and increased dopamine-related RS FC in the left middle temporal gyrus and cerebellum (lobule VI) vs. NF patients. In the noradrenaline-related network, F-pedMS patients showed decreased RS FC in the left superior parietal lobule and increased RS FC in the right thalamus vs. HC and NF-pedMS. Compared to HC, F-pedMS patients also showed decreased RS FC in the right calcarine cortex and increased RS FC in the right middle frontal gyrus. In the serotonin-related network, F-pedMS patients showed decreased RS FC in the right angular gyrus and increased RS FC in the right postcentral gyrus vs. NF-pedMS patients. Discussion In pedMS, fatigue is associated with specific monoaminergic network abnormalities, providing pathological markers for this bothersome symptom and putative targets for its treatment.

Dysregulation of monoaminergic networks might have a role in the pathogenesis of fatigue in multiple sclerosis (MS). We investigated longitudinal changes of resting state (RS) functional connectivity (FC) in monoaminergic networks and their association with the development of fatigue in MS. Eighty-nine MS patients and 49 age- and sex-matched healthy controls (HC) underwent neurological, fatigue, and RS functional MRI assessment at baseline and after a median follow-up of 1.3 years (interquartile range = 1.01–2.01 years). Monoaminergic-related RS FC was estimated with an independent component analysis constrained to PET atlases for dopamine (DA), noradrenaline (NA), and serotonin (5-HT) transporters. At baseline, 24 (27%) MS patients were fatigued (F) and 65 were not fatigued (NF). Of these, 22 (34%) developed fatigue (DEV-FAT) at follow-up and 43 remained not fatigued (NO-FAT). At baseline, F-MS patients showed increased monoaminergic-related RS FC in the caudate nucleus vs NF-MS and in the hippocampal, postcentral, temporal, and occipital cortices vs NF-MS and HC. Moreover, F-MS patients exhibited decreased RS FC in the frontal cortex vs NF-MS and HC, and in the thalamus vs NF-MS. During the follow-up, no RS FC changes were observed in HC. NO-FAT patients showed limited DA-related RS FC modifications, whereas DEV-FAT MS patients showed increased DA-related RS FC in the left hippocampus, significant at time-by-group interaction analysis. In the NA-related network, NO-FAT patients showed decreased RS FC over time in the left superior frontal gyrus. This region showed increased RS FC in both DEV-FAT and F-MS patients; this divergent behavior was significant at time-by-group interaction analysis. Finally, DEV-FAT MS patients presented increased 5-HT-related RS FC in the angular and middle occipital gyri, while this latter region showed decreased 5-HT-related RS FC during the follow-up in F-MS patients. In MS patients, distinct patterns of alterations were observed in monoaminergic networks based on their fatigue status. Fatigue was closely linked to specific changes in the basal ganglia and hippocampal, superior frontal, and middle occipital cortices.

Background Cognitive impairment is common in multiple sclerosis (MS), yet the application of diagnostic frameworks of Neurocognitive Disorders (NCDs) is limited. Additionally, the integration of multimodal data for predicting cognitive outcomes using artificial intelligence (AI) remains underexplored. This study aimed to characterize NCDs in MS and predict cognitive worsening using an explainable deep learning model trained on MRI and clinical data. Methods Two‐hundred twenty‐four MS patients and 115 healthy controls (HC) underwent 3.0 T MRI and clinical assessment at baseline. MS patients also completed neuropsychological testing, including estimation of z‐cognitive reserve, at baseline and after a median follow‐up of 3.4 (interquartile range = [2.0; 6.1]) years. MS patients were classified as Mild or Major NCD according to the Diagnostic and Statistical Manual of Mental Disorders criteria at baseline, and as “stable” or “worsened” based on cognitive changes at follow‐up. A deep learning model was trained on baseline T1‐weighted MRI, demographic, clinical, and brain volumetric data to predict cognitive decline, with explainability methods used to interpret the model's decisions. Results At baseline, 4% of patients had Mild and 11% Major NCD. At follow‐up, 12% showed cognitive decline. The deep learning model predicted follow‐up cognitive status with 90% accuracy. Explainability models identified the most relevant predictors, in order of importance: cortical gray matter volume, age, thalamic and hippocampal volumes, T2 lesion volume, and z‐cognitive reserve. Conclusions The proposed multimodal AI approach demonstrated robust performance and highlighted relevant brain regions associated with cognitive worsening, underscoring its potential for personalized cognitive assessment and monitoring in MS. This study combined clinical, demographic, and MRI data from 224 multiple sclerosis (MS) patients using an explainable hybrid deep learning model to assess the prevalence of Mild and Major Neurocognitive Disorders and predict future cognitive decline. The model showed high accuracy (AUC = 0.89) and low uncertainty, identifying cortical and frontal lobe volumes as key predictors of cognitive impairment. These findings highlight the potential of explainable artificial intelligence to enable personalized monitoring, biologically informed insights, and early intervention in MS.