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Early-onset Alzheimer's disease (EOAD) is less investigated than the more common late-onset Alzheimer's disease (LOAD) despite its more aggressive course. A cortical signature of EOAD was recently proposed and may facilitate EOAD investigation. Here, we aimed to validate this proposed MRI biomarker of EOAD neurodegeneration in an Appalachian clinical cohort. We also compared differences in EOAD signature atrophy in participants with biomarker-positive EOAD, LOAD, early-onset non-AD pathologies, and cognitively normal individuals. Cortical thinning was reliably detected in eight of nine signature areas of persons with EOAD relative to cognitively normal individuals despite very early disease stage. Additionally, individuals with EOAD showed thinner cortex in most signature regions relative to those with early-onset non-AD pathologies. EOAD and LOAD showed similar cortical atrophy within most EOAD signature regions. Whole-brain vertex-wise cortical analyses supported these findings. Furthermore, signature cortical atrophy showed expected relationships with measures of global and specific cognitive and functional status. This investigation further validates and expands upon the recently defined EOAD signature and suggests its robustness within a rural population, even at early disease stage. Larger scale and longitudinal studies employing this marker of EOAD neurodegeneration are needed to further understand clinical effects and appropriate management of persons with EOAD., (© The Author(s) 2024. Published by Oxford University Press.)

Background: While the cognitive hallmark of typical Alzheimer disease (AD) is impaired memory consolidation, increasing evidence suggests that the frontal lobes and associated executive functions are also impacted., Objective: We examined two neurobehavioral executive function tasks and associations with cortical thickness in patients diagnosed with mild cognitive impairment (MCI), suspected AD dementia, and a healthy control group., Methods: First, we compared group performances on a go/no-go (GNG) task and on Luria's Fist-Edge-Palm (FEP) motor sequencing task. We then examined correlations between neurobehavioral task performance and the thickness of frontal cortical regions, AD signature regions, broader unbiased brain regions, and white matter hyperintensities (WMH)., Results: Participants with MCI performed worse than healthy controls, but better than participants with suspected AD dementia on both tasks. Both GNG and FEP (to a slightly greater extent) tasks showed diffuse associations with most AD signature regions and multiple additional regions within the temporal, parietal, and occipital cortices. Similarly, both tasks showed significant associations with all other cognitive tasks examined. Of the frontal regions examined, only the middle frontal gyrus and pars opercularis were associated with performance on these tasks. Interactions between the precuneus and transtemporal gyri were most predictive of GNG task performance, while the interaction between superior temporal and lingual gyri was most predictive of FEP task performance., Conclusion: This study replicates difficulties with both GNG and FEP tasks in participants with MCI and AD dementia. Both tasks showed widespread associations with the cortical thickness of various brain structures rather than localizing to frontal regions, consistent with the diffuse nature of AD., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

The thalamus serves as the central relay station for the brain. It processes and relays sensory and motor signals between different subcortical regions and the cerebral cortex and it can be divided into several neuronal clusters referred to as nuclei. Each of these can possibly be subdivided into sub-nuclei. Accurate and reliable identification of thalamic nuclei is important for surgical interventions and neuroanatomical studies. This is however a challenging task because the small size of the nuclei and the lack of contrast over the thalamus region in clinically acquired images does not permit the visualization of their boundaries. A number of methods have been developed for thalamus parcellation but the vast majority of these relies on diffusion imaging or functional imaging. The low resolution of these images only permit localizing the largest nuclei. In this work we propose a method to segment smaller nuclei. We first present a protocol to build histological-like atlases from a series of high-field (7 Tesla) MR images acquired with different pulse sequences that each permits to visualize the boundaries of a subset of the nuclei. We use this protocol to scan 9 subjects and we manually delineate 23 thalamic nuclei following the Morel atlas naming convention for each of these subjects. Manual contours for the nuclei are subsequently utilized to create statistical shape models. With these data, we compare four methods for the segmentation of thalamic nuclei in 3 T images we have also acquired for the 9 subjects included in the study: (1) single atlas, (2) multi atlas, (3) statistical shape, and (4) hierarchical statistical shape in which thalamic nuclei are hierarchically fitted to the images, starting from the largest ones. Results of a leave-one-out validation study conducted on the nine image sets we have acquired show that the multi atlas approach improves upon the single atlas approach for most nuclei. Segmentations obtained with the hierarchical statistical shape model yield the highest accuracy, with dice coefficients ranging from 0.53 to 0.90, mean surface errors from 0.27 mm to 0.64 mm, and maximum surface errors from 1.31 mm to 2.52 mm for all nuclei averaged across test cases. This suggests the feasibility of using such approach for localizing thalamic substructures in clinically acquired MR volumes. It may have a direct impact on surgeries such as Deep Brain Stimulation procedures that require the implantation of stimulating electrodes in specific thalamic nuclei., (Copyright © 2019 Elsevier Inc. All rights reserved.)

The basal ganglia and limbic system, particularly the thalamus, putamen, internal and external globus pallidus, substantia nigra, and sub-thalamic nucleus, comprise a clinically relevant signal network for Parkinson's disease. In order to manually trace these structures, a combination of high-resolution and specialized sequences at 7 T are used, but it is not feasible to routinely scan clinical patients in those scanners. Targeted imaging sequences at 3 T have been presented to enhance contrast in a select group of these structures. In this work, we show that a series of atlases generated at 7 T can be used to accurately segment these structures at 3 T using a combination of standard and optimized imaging sequences, though no one approach provided the best result across all structures. In the thalamus and putamen, a median Dice Similarity Coefficient (DSC) over 0.88 and a mean surface distance <1.0 mm were achieved using a combination of T1 and an optimized inversion recovery imaging sequences. In the internal and external globus pallidus a DSC over 0.75 and a mean surface distance <1.2 mm were achieved using a combination of T1 and inversion recovery imaging sequences. In the substantia nigra and sub-thalamic nucleus a DSC of over 0.6 and a mean surface distance of <1.0 mm were achieved using the inversion recovery imaging sequence. On average, using T1 and optimized inversion recovery together significantly improved segmentation results than over individual modality (p < 0.05 Wilcoxon sign-rank test)., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Introduction: Laser interstitial thermal therapy (LITT) is a novel MR thermometry-guided thermoablative tool revolutionizing the clinical management of brain tumors. A limitation of LITT is our inability to estimate a priori how tissues will respond to thermal energy, which hinders treatment planning and delivery. The aim of this study was to determine whether brain tumor LITT ablation dynamics may be predicted by features of the preoperative MRI and the relevance of these data, if any, to the recurrence of metastases after LITT., Methods: Intraoperative thermal damage estimate (TDE) map pixels representative of irreversible damage were retrospectively quantified relative to ablation onset for 101 LITT procedures. Raw TDE pixel counts and TDE pixel counts modelled with first order dynamics were related to eleven independent variables derived from the preoperative MRI, demographics, laser settings, and tumor pathology. Stepwise regression analysis generated predictive models of LITT dynamics, and leave-one-out cross validation evaluated the accuracy of these models at predicting TDE pixel counts solely from the independent variables. Using a deformable atlas, TDE maps were co-registered to the immediate post-ablation MRI, allowing comparison of predicted and actual ablation sizes., Results: Brain tumor TDE pixel counts modelled with first order dynamics, but not raw pixel counts, are correlated with the independent variables. Independent variables showing strong relations to the TDE pixel measures include T1 gadolinium and T2 signal, perfusion, and laser power. Associations with tissue histopathology are minimal. Leave-one-out analysis demonstrates that predictive models using these independent variables account for 77% of the variance observed in TDE pixel counts. Analysis of metastases treated revealed a trend towards the over-estimation of LITT effects by TDE maps during rapid ablations, which was associated with tumor recurrence., Conclusions: Features of the preoperative MRI are predictive of LITT ablation dynamics and could eventually be used to improve the clinical efficacy with which LITT is delivered to brain tumors.

Magnetic resonance imaging (MRI) is an important tool for analysis of deep brain grey matter structures. However, analysis of these structures is limited due to low intensity contrast typically found in whole brain imaging protocols. Herein, we propose a big data registration-enhancement (BDRE) technique to augment the contrast of deep brain structures using an efficient large-scale non-rigid registration strategy. Direct validation is problematic given a lack of ground truth data. Rather, we validate the usefulness and impact of BDRE for multi-atlas (MA) segmentation on two sets of structures of clinical interest: the thalamic nuclei and hippocampal subfields. The experimental design compares algorithms using T1-weighted 3 T MRI for both structures (and additional 7 T MRI for the thalamic nuclei) with an algorithm using BDRE. As baseline comparisons, a recent denoising (DN) technique and a super-resolution (SR) method are used to preprocess the original 3 T MRI. The performance of each MA segmentation is evaluated by the Dice similarity coefficient (DSC). BDRE significantly improves mean segmentation accuracy over all methods tested for both thalamic nuclei (3 T imaging: 9.1%; 7 T imaging: 15.6%; DN: 6.9%; SR: 16.2%) and hippocampal subfields (3 T T1 only: 8.7%; DN: 8.4%; SR: 8.6%). We also present DSC performance for each thalamic nucleus and hippocampal subfield and show that BDRE can help MA segmentation for individual thalamic nuclei and hippocampal subfields. This work will enable large-scale analysis of clinically relevant deep brain structures from commonly acquired T1 images., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Objective: Seizures in temporal lobe epilepsy (TLE) disturb brain networks and lead to connectivity disturbances. We previously hypothesised that recurrent seizures in TLE may lead to abnormal connections involving subcortical activating structures including the ascending reticular activating system (ARAS), contributing to neocortical dysfunction and neurocognitive impairments. However, no studies of ARAS connectivity have been previously reported in patients with epilepsy., Methods: We used resting-state functional MRI recordings in 27 patients with TLE (67% right sided) and 27 matched controls to examine functional connectivity (partial correlation) between eight brainstem ARAS structures and 105 cortical/subcortical regions. ARAS nuclei included: cuneiform/subcuneiform, dorsal raphe, locus coeruleus, median raphe, parabrachial complex, pontine oralis, pedunculopontine and ventral tegmental area. Connectivity patterns were related to disease and neuropsychological parameters., Results: In control subjects, regions showing highest connectivity to ARAS structures included limbic structures, thalamus and certain neocortical areas, which is consistent with prior studies of ARAS projections. Overall, ARAS connectivity was significantly lower in patients with TLE than controls (p<0.05, paired t-test), particularly to neocortical regions including insular, lateral frontal, posterior temporal and opercular cortex. Diminished ARAS connectivity to these regions was related to increased frequency of consciousness-impairing seizures (p<0.01, Pearson's correlation) and was associated with impairments in verbal IQ, attention, executive function, language and visuospatial memory on neuropsychological evaluation (p<0.05, Spearman's rho or Kendell's tau-b)., Conclusions: Recurrent seizures in TLE are associated with disturbances in ARAS connectivity, which are part of the widespread network dysfunction that may be related to neurocognitive problems in this devastating disorder., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

Identification of error in nonrigid registration is a critical problem in the medical image processing community. We recently proposed an algorithm that we call "Assessing Quality Using Image Registration Circuits" (AQUIRC) to identify nonrigid registration errors and have tested its performance using simulated cases. In this paper, we extend our previous work to assess AQUIRC's ability to detect local nonrigid registration errors and validate it quantitatively at specific clinical landmarks, namely the anterior commissure and the posterior commissure. To test our approach on a representative range of error we utilize five different registration methods and use 100 target images and nine atlas images. Our results show that AQUIRC's measure of registration quality correlates with the true target registration error (TRE) at these selected landmarks with an R(2)=0.542. To compare our method to a more conventional approach, we compute local normalized correlation coefficient (LNCC) and show that AQUIRC performs similarly. However, a multi-linear regression performed with both AQUIRC's measure and LNCC shows a higher correlation with TRE than correlations obtained with either measure alone, thus showing the complementarity of these quality measures. We conclude the paper by showing that the AQUIRC algorithm can be used to reduce registration errors for all five algorithms.

The anterior and posterior commissures (AC and PC) typically form the reference points of the stereotactic coordinate system. Hence any discussion of target localization is limited by the variability of AC and PC selection. In an earlier study, which was performed using manual selections of AC and PC by 43 neurosurgeons, we showed that intersurgeon variability has a substantial impact on the localization of deep brain stimulation targets. We have developed and validated a fully automatic and robust AC and PC selection system that can be routinely used clinically. In this study, we show that this system is capable of localizing the AC and PC points with an accuracy that is better than that achieved clinically by manual selection, 0.65 mm (95% confidence interval: 0.56-0.79) versus 1.21 mm (95% confidence interval: 0.91-1.47) for AC and 0.56 mm (95% confidence interval: 0.46-0.66) versus 1.06 mm (95% confidence interval: 0.82-1.26) for PC., (2009 S. Karger AG, Basel.)

To help surgeons to pre-operatively select the target location for DBS electrodes, functional atlases based on intra-operatively acquired data have been created in the past. Recently, many groups have reported on the occurrence of brain shift in stereotactic surgery and its impact on the procedure but not on the creation of such atlases. Due to brain shift, the pre- and intra-operative coordinates of anatomic structures are different. When building large population atlases, which rely on pre-operative images for normalization purposes, it is thus necessary to correct for this difference. In this paper, we propose a method to achieve this. We show evidence that electrophysiological maps built using corrected and uncorrected data are different and that the maps created using shift-corrected data correlate better than those created using uncorrected data with the final position of the implant. These findings suggest that brain-shift correction of intra-operatively recorded data is feasible for the construction of accurate shift-corrected electrophysiological atlases.