Tools for post-operative localization of deep brain stimulation (DBS) electrodes may be of major benefit in the evaluation of the stimulation area. However, little is known about their precision. This study compares 3 different software packages used for DBS electrode localization. T1-weighted MRI images before and after the implantation of the electrodes into the subthalamic nucleus for DBS in 105 Parkinson's disease patients were processed using the pipelines implemented in Lead-DBS, SureTune4, and Brainlab. Euclidean distance between active contacts determined by individual software packages and in repeated processing by the same and by a different operator was calculated. Furthermore, Dice coefficient for overlap of volume of tissue activated (VTA) was determined for Lead-DBS. Medians of Euclidean distances between estimated active contact locations in inter-software package comparison ranged between 1.5 mm and 2 mm. Euclidean distances in within-software package intra- and inter-rater assessments were 0.6-1 mm and 1-1.7 mm, respectively. Median intra- and inter-rater Dice coefficients for VTAs were 0.78 and 0.75, respectively. Since the median distances are close to the size of the target nucleus, any clinical use should be preceded by careful review of the outputs.
- MeSH
- Deep Brain Stimulation * methods instrumentation MeSH
- Electrodes, Implanted * MeSH
- Middle Aged MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Subthalamic Nucleus surgery MeSH
- Parkinson Disease * therapy MeSH
- Aged MeSH
- Software MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
Patients with cardioembolic ischemic stroke are commonly prescribed direct oral anticoagulants (DOACs), such as dabigatran (a direct thrombin inhibitor) and factor Xa inhibitors (e.g., apixaban and rivaroxaban), or warfarin to reduce the risk of recurrent stroke. A major concern in anticoagulant therapy is the risk of intracerebral hemorrhage, which is associated with a high mortality rate. Cerebral microbleeds (MBs), small asymptomatic brain hemorrhages detectable by susceptibility-weighted imaging (SWI) on magnetic resonance imaging (MRI), are associated with increased hemorrhagic stroke risk. This study evaluated the incidence of new MBs during 1 year of anticoagulation therapy in patients after cardioembolic stroke. Patients indicated for anticoagulant therapy after cardioembolic stroke and monitored in the cerebrovascular outpatient clinic of our department underwent brain MRI at baseline and after 1 year of therapy. The occurrence of new MBs was assessed using SWI sequences. MBs were categorized based on location into 3 groups: deep (dMBs), lobar (lMBs), and infratentorial (iMBs). A total of 79 patients were included, 53 of whom were male (67.1%), with a median age of 71 years (IQR: 64-76). The majority of patients (n = 50, 63.3%) were treated with apixaban, 16 patients (20.3%) with dabigatran, and 13 patients (16.5%) with warfarin. Baseline MRI revealed MBs in 17 patients (21.5%), including dMBs in 2, lMBs in 16, and iMBs in 2 patients. Follow-up MRI showed new MBs in 8 patients (10.1%), with new dMBs in 1, lMBs in 5, and iMBs in 4 patients. No statistically significant differences were observed in MBs the incidence of new MBs between anticoagulant groups (P = .912). Over 1 year of anticoagulant therapy, new MBs were detected in 10.1% of patients, predominantly in lobar and infratentorial regions. No differences in the incidence of new MBs were identified between the different anticoagulant groups.
- MeSH
- Anticoagulants * adverse effects therapeutic use MeSH
- Cerebral Hemorrhage * chemically induced diagnostic imaging epidemiology MeSH
- Stroke * prevention & control MeSH
- Dabigatran adverse effects therapeutic use MeSH
- Incidence MeSH
- Middle Aged MeSH
- Humans MeSH
- Magnetic Resonance Imaging methods MeSH
- Pyrazoles adverse effects therapeutic use MeSH
- Pyridones adverse effects therapeutic use MeSH
- Secondary Prevention * methods MeSH
- Aged MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Observational Study MeSH
BACKGROUND AND OBJECTIVES: Frontotemporal lobar degeneration (FTLD) as the second most common dementia encompasses a range of syndromes and often shows overlapping symptoms with other subtypes or neurodegenerative diseases, which poses a significant clinical diagnostic challenge. Recent advancements in artificial intelligence (AI), specifically the application of machine learning (ML) algorithms to neuroimaging, have significantly progressed in addressing this challenge. This study aims to assess the diagnostic and predictive efficacy of neuroimaging feature-based AI algorithms for FTLD. METHODS: We conducted a systematic review and meta-analysis following PRISMA guidelines. We searched Pubmed, Scopus, and Web of Science for English-language, peer-reviewed studies using the following three umbrella terms: artificial intelligence, frontotemporal lobar degeneration, and neuroimaging modality. Our survey focused on computer-aided diagnosis for FTLD, employing machine/deep learning with neuroimaging radiomic features. RESULTS: The meta-analysis includes 75 articles with 20,601 subjects, including 8,051 FTLD patients. The results reveal that FTLD can be automatically classified against healthy controls (HC) with pooled sensitivity and specificity of 86% and 89%, respectively. Likewise, FTLD versus Alzheimer's disease (AD) classification exhibits pooled sensitivity and specificity of 84% and 81%, while FTLD versus Parkinson's disease (PD) demonstrates pooled sensitivity and specificity of 84% and 75%, respectively. Classification performance distinguishing FTLD from atypical Parkinsonian syndromes (APS) showed pooled sensitivity and specificity of 84% and 79%, respectively. Multiclass classification sensitivity ranges from 42% to 100%, with lower sensitivity occurring in higher class distinctions (e.g., 5-class and 11-class). DISCUSSION: Our study demonstrates the effectiveness of utilizing neuroimaging features to distinguish FTLD from HC, AD, APS, and PD in binary classification. Utilizing deep learning with multimodal neuroimaging data to differentiate FTLD subtypes and perform multiclassification among FTLD and other neurodegenerative disease holds promise for expediting diagnosis. In sum, the meta-analysis supports translation of machine learning tools in combination with imaging to clinical routine paving the way to precision medicine.
INTRODUCTION: Deep brain stimulation (DBS) of the internal globus pallidus (GPi) is a well-established, effective treatment for dystonia. Substantial variability of therapeutic success has been the one of the drivers of an ongoing debate about proper stimulation site and settings, with several indications of the notional sweet spot pointing to the lower GPi or even subpallidal area. METHODS: The presented patient-blinded, random-order study with cross-sectional verification against healthy controls enrolled 17 GPi DBS idiopathic, cervical or generalised dystonia patients to compare the effect of the stimulation in the upper and lower GPi area, with the focus on sensorimotor network connectivity and local activity measured using functional magnetic resonance. RESULTS: Stimulation brought both these parameters to levels closer to the state detected in healthy controls. This effect was much more pronounced during the stimulation in the lower GPi area or beneath it than in slightly higher positions, with stimulation-related changes detected by both metrics of interest in the sensorimotor cortex, striatum, thalamus and cerebellum. CONCLUSIONS: All in all, this study not only replicated the results of previous studies on GPi DBS as a modality restoring sensorimotor network connectivity and local activity in dystonia towards the levels in healthy population, but also showed that lower GPi area or even subpallidal structures, be it white matter or even small, but essential nodes in the zona incerta as nucleus basalis of Meynert, are important regions to consider when programming DBS in dystonia patients.
- MeSH
- Adult MeSH
- Dystonic Disorders therapy physiopathology diagnostic imaging MeSH
- Dystonia therapy physiopathology diagnostic imaging MeSH
- Globus Pallidus * diagnostic imaging physiopathology MeSH
- Deep Brain Stimulation * methods MeSH
- Middle Aged MeSH
- Humans MeSH
- Magnetic Resonance Imaging * methods MeSH
- Cross-Sectional Studies MeSH
- Aged MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Randomized Controlled Trial MeSH
IMPORTANCE: Baseline cerebral microbleeds (CMBs) and APOE ε4 allele copy number are important risk factors for amyloid-related imaging abnormalities in patients with Alzheimer disease (AD) receiving therapies to lower amyloid-β plaque levels. OBJECTIVE: To provide prevalence estimates of any, no more than 4, or fewer than 2 CMBs in association with amyloid status, APOE ε4 copy number, and age. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used data included in the Amyloid Biomarker Study data pooling initiative (January 1, 2012, to the present [data collection is ongoing]). Data from 15 research and memory clinic studies were pooled and harmonized. Participants included individuals for whom data on age, cognitive status, amyloid status, and presence of CMBs were available. Data were analyzed from October 22, 2023, to April 26, 2024. MAIN OUTCOMES AND MEASURES: The main outcomes were age, cognitive status, amyloid status and presence, location, and number of CMBs. Presence of amyloid pathology was determined based on 42 amino acid-long form of amyloid-β peptide (Aβ42) levels in cerebrospinal fluid or on amyloid-positron emission tomography. Presence and, in a subset, location (lobar vs deep) and number of CMBs were determined on magnetic resonance imaging (locally with visual rating). RESULTS: Among 4080 participants included in the analysis, the mean (SD) age was 66.5 (8.9) years, and 2241 (54.9%) were female. A total of 2973 participants had no cognitive impairment (cognitive unimpairment [CU]), and 1107 had mild cognitive impairment (MCI) or AD dementia (ADD). One thousand five hundred and thirteen participants (37.1%) had amyloid pathology, 1368 of 3599 (38.0%) with data available were APOE ε4 carriers, and 648 (15.9%) had CMBs. In the CU group, amyloid pathology and APOE ε4 copy number were not associated with presence of any, no more than 4, or fewer than 2 CMBs but were associated with increased odds of lobar CMBs (odds ratio [OR] for amyloid, 1.42 [95% CI, 1.20-1.69], P < .001; OR for 2 vs 0 alleles, 1.81 [95% CI, 1.19-2.74], P = .006; OR for 1 vs 0 alleles, 1.10 [95% CI, 0.83-1.46], P = .49; and OR for 2 vs 1 allele, 1.64 [95% CI, 0.90-2.97], P = .11; overall P = .02). In the MCI-ADD group, amyloid pathology was associated with presence of any CMBs (OR, 1.51 [95% CI, 1.17-1.96], P = .002), no more than 4 CMBs (OR, 1.44 [95% CI, 1.18-1.82], P = .002), and fewer than 2 CMBs (OR 1.34 [95% CI, 1.03-1.74], P = .03) but not lobar CMBs. APOE ε4 copy number was associated with presence of any (OR for 2 vs 0 alleles, 1.72 [95% CI, 0.88-3.35], P = .11; OR for 1 vs 0 alleles, 0.78 [95% CI, 0.59-1.04], P = .09; and OR for 2 vs 1 allele, 2.20 [95% CI, 1.32-3.67], P = .002; overall P < .001) and no more than 4 CMBs (OR for 2 vs 0 alleles, 1.31 [95% CI, 0.64-2.68], P = .45; OR for 1 vs 0 alleles, 0.75 [95% CI, 0.54-1.04], P = .08; and OR for 2 vs 1 allele, 1.76 [95% CI, 0.97-3.19], P = .06; overall P = .03) but not with fewer than 2 or lobar CMBs. Prevalence estimates of CMBs ranged from 6% at 50 years of age in a non-APOE ε4 allele carrier with no amyloid pathology and no cognitive impairment to 52% at 90 years of age in an APOE ε4 homozygote carrier with amyloid pathology and cognitive impairment. CONCLUSIONS AND RELEVANCE: In this cross-sectional study of 4080 participants, prevalence estimates of CMBs were associated with amyloid status, APOE ε4 copy number, and age. CMB prevalence estimates may help inform safety evaluations for antiamyloid clinical trials.
- MeSH
- Alzheimer Disease * epidemiology genetics MeSH
- Amyloid beta-Peptides * metabolism cerebrospinal fluid MeSH
- Plaque, Amyloid pathology MeSH
- Apolipoprotein E4 genetics MeSH
- Biomarkers * cerebrospinal fluid MeSH
- Cerebral Hemorrhage * epidemiology MeSH
- Middle Aged MeSH
- Humans MeSH
- Magnetic Resonance Imaging methods MeSH
- Positron-Emission Tomography MeSH
- Cross-Sectional Studies MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
Detection and segmentation of brain abnormalities using Magnetic Resonance Imaging (MRI) is an important task that, nowadays, the role of AI algorithms as supporting tools is well established both at the research and clinical-production level. While the performance of the state-of-the-art models is increasing, reaching radiologists and other experts' accuracy levels in many cases, there is still a lot of research needed on the direction of in-depth and transparent evaluation of the correct results and failures, especially in relation to important aspects of the radiological practice: abnormality position, intensity level, and volume. In this work, we focus on the analysis of the segmentation results of a pre-trained U-net model trained and validated on brain MRI examinations containing four different pathologies: Tumors, Strokes, Multiple Sclerosis (MS), and White Matter Hyperintensities (WMH). We present the segmentation results for both the whole abnormal volume and for each abnormal component inside the examinations of the validation set. In the first case, a dice score coefficient (DSC), sensitivity, and precision of 0.76, 0.78, and 0.82, respectively, were found, while in the second case the model detected and segmented correct (True positives) the 48.8% (DSC ≥ 0.5) of abnormal components, partially correct the 27.1% (0.05 > DSC > 0.5), and missed (False Negatives) the 24.1%, while it produced 25.1% False Positives. Finally, we present an extended analysis between the True positives, False Negatives, and False positives versus their position inside the brain, their intensity at three MRI modalities (FLAIR, T2, and T1ce) and their volume.
- Publication type
- Journal Article MeSH
BACKGROUND: Electrical stimulation involving temporal interference of two different kHz frequency sinusoidal electric fields (temporal interference (TI)) enables non-invasive deep brain stimulation, by creating an electric field that is amplitude modulated at the slow difference frequency (within the neural range), at the target brain region. OBJECTIVE: Here, we investigate temporal interference neural stimulation using square, rather than sinusoidal, electric fields that create an electric field that is pulse-width, but not amplitude, modulated at the difference frequency (pulse-width modulated temporal interference, (PWM-TI)). METHODS/RESULTS: We show, using ex-vivo single-cell recordings and in-vivo calcium imaging, that PWM-TI effectively stimulates neural activity at the difference frequency at a similar efficiency to traditional TI. We then demonstrate, using computational modelling, that the PWM stimulation waveform induces amplitude-modulated membrane potential depolarization due to the membrane's intrinsic low-pass filtering property. CONCLUSIONS: PWM-TI can effectively drive neural activity at the difference frequency. The PWM-TI mechanism involves converting an envelope amplitude-fixed PWM field to an amplitude-modulated membrane potential via the low-pass filtering of the passive neural membrane. Unveiling the biophysics underpinning the neural response to complex electric fields may facilitate the development of new brain stimulation strategies with improved precision and efficiency.
- MeSH
- Electric Stimulation MeSH
- Brain * MeSH
- Computer Simulation MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
BACKGROUND AND OBJECTIVES: Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific effects. In this study, we investigated whether a disease-specific model might complement the brain-age gap (BAG) by capturing aspects unique to MS. METHODS: In this retrospective study, we collected 3D T1-weighted brain MRI scans of PwMS to build (1) a cross-sectional multicentric cohort for age and disease duration (DD) modeling and (2) a longitudinal single-center cohort of patients with early MS as a clinical use case. We trained and evaluated a 3D DenseNet architecture to predict DD from minimally preprocessed images while age predictions were obtained with the DeepBrainNet model. The brain-predicted DD gap (the difference between predicted and actual duration) was proposed as a DD-adjusted global measure of MS-specific brain damage. Model predictions were scrutinized to assess the influence of lesions and brain volumes while the DD gap was biologically and clinically validated within a linear model framework assessing its relationship with BAG and physical disability measured with the Expanded Disability Status Scale (EDSS). RESULTS: We gathered MRI scans of 4,392 PwMS (69.7% female, age: 42.8 ± 10.6 years, DD: 11.4 ± 9.3 years) from 15 centers while the early MS cohort included 749 sessions from 252 patients (64.7% female, age: 34.5 ± 8.3 years, DD: 0.7 ± 1.2 years). Our model predicted DD better than chance (mean absolute error = 5.63 years, R2 = 0.34) and was nearly orthogonal to the brain-age model (correlation between DD and BAGs: r = 0.06 [0.00-0.13], p = 0.07). Predictions were influenced by distributed variations in brain volume and, unlike brain-predicted age, were sensitive to MS lesions (difference between unfilled and filled scans: 0.55 years [0.51-0.59], p < 0.001). DD gap significantly explained EDSS changes (B = 0.060 [0.038-0.082], p < 0.001), adding to BAG (ΔR2 = 0.012, p < 0.001). Longitudinally, increasing DD gap was associated with greater annualized EDSS change (r = 0.50 [0.39-0.60], p < 0.001), with an incremental contribution in explaining disability worsening compared with changes in BAG alone (ΔR2 = 0.064, p < 0.001). DISCUSSION: The brain-predicted DD gap is sensitive to MS-related lesions and brain atrophy, adds to the brain-age paradigm in explaining physical disability both cross-sectionally and longitudinally, and may be used as an MS-specific biomarker of disease severity and progression.
- MeSH
- Deep Learning * MeSH
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Longitudinal Studies MeSH
- Magnetic Resonance Imaging * MeSH
- Brain * diagnostic imaging pathology MeSH
- Neurodegenerative Diseases diagnostic imaging MeSH
- Cross-Sectional Studies MeSH
- Retrospective Studies MeSH
- Multiple Sclerosis * diagnostic imaging pathology MeSH
- Aging * pathology physiology MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Multicenter Study MeSH
Závěrečná zpráva o řešení grantu Agentury pro zdravotnický výzkum MZ ČR
nestr.
Hluboká mozková stimulace (DBS) u Parkinsonovy nemoci (PN) je standardní léčebnou metodou pozdního a intermediálního stádia. Vývoj PN je spojen s rozvojem řady non-motorických symptomů, které v klinickém obraze mnohdy dominují, a které se mohou během DBS STN významně zhoršit. Mezi „tvrdá“ kritéria, na základě kterých bývá pacient z indikačního procesu DBS obvykle vyloučen, jsou projevy atypických parkinsonských syndromů, symptomy u PN s klinickým obrazem demence, floridní deprese, opakovanými psychotickými stavy, posturální nestabilitou nebo poruchou chůze a to i přes optimálně vedenou dopaminergní léčbu. „Měkká“ vylučovací kritéria jsou spojena s vyšším biologickým věkem, výskytem mírné kognitivní poruchy, anamnézou sporadických pre-psychotických symptomů či předchozí úzkostně-depresivní epizodou. Záměrem předkládaného projektu je snaha prověřit stávající měkká indikační kritéria s cílem stanovit důležitost jednotlivých ukazatelů v predikci budoucího úspěchu DBS STN současně se snahou doplnit je o kritéria nová, opřená o multimodální předoperační nálezy.; Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a common treatment for intermediate and late stages of Parkinson’s disease (PD). The development of PD is associated with the progression of several non-motor symptoms that may predominate the clinical picture and which may substantially worsen in the course of STN DBS. Among the ‘hard’ criteria that exclude patients from the DBS program are atypical parkinsonian syndromes, dementia, actual depression, recurrent psychotic conditions, and postural instability with gait disorder despite optimal dopaminergic therapy. On the other hand, ‘soft’ exclusion criteria are associated with axial motor symptoms, higher biological age, the occurrence of mild cognitive impairment, a history of sporadic pre-psychotic symptoms, or a preceding anxiety-depressive episode. The aim of this project is to challenge the ‘soft’ criteria and to establish the importance of individual indicators in predicting the future of a STN DBS outcome as well as to define new additional criteria based on multi-modal preoperative testing.
- Keywords
- biomarkery, biomarkers, Parkinsonova nemoc, Parkinson Disease, mírná kognitivní porucha, mild cognitive impairment, řeč, subthalamic nucleus, hluboká mozková stimulace, deep brain stimulation, Oční pohyby, Eye movements, subtalamické jádro, resting state fMRI, mikroregistrace, non-motorické příznaky, axiální příznaky, DBS, resting state fMRI, speech production, microrecording, non-motor symptoms, axial symptoms, DBS,
- NML Publication type
- závěrečné zprávy o řešení grantu AZV MZ ČR
Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpoints in neuroscience digital pathology. Prior experience and domain expert knowledge are the key aspects for image annotation quality and consistency. At present, image annotation is often achieved manually by certified pathologists or trained technicians, limiting the total throughput of studies performed at neuroscience digital pathology labs. It may also mean that simpler and quicker methods of examining tissue samples are used by non-pathologists, especially in the early stages of research and preclinical studies. To address these limitations and to meet the growing demand for image analysis in a pharmaceutical setting, we developed AnNoBrainer, an open-source software tool that leverages deep learning, image registration, and standard cortical brain templates to automatically annotate individual brain regions on 2D pathology slides. Application of AnNoBrainer to a published set of pathology slides from transgenic mice models of synucleinopathy revealed comparable accuracy, increased reproducibility, and a significant reduction (~ 50%) in time spent on brain annotation, quality control and labelling compared to trained scientists in pathology. Taken together, AnNoBrainer offers a rapid, accurate, and reproducible automated annotation of mouse brain images that largely meets the experts' histopathological assessment standards (> 85% of cases) and enables high-throughput image analysis workflows in digital pathology labs.
