Probabilistic Atlas Research Articles

Subcortical white matter differences according to presence of disorders of consciousness in hypoxic-ischemic brain injury: a tract-based spatial statistics study.

We investigated differences in subcortical white matter according to the presence disorders of consciousness (DOC) in patients with hypoxic-ischemic brain injury (HI-BI), using tract-based spatial statistics (TBSS). Thirty-two consecutive patients with HI-BI were recruited. The patients were assigned in group A [preserved consciousness (Glasgow Coma Scale: 15 and Coma Recovery Scale-revised (CRS-R): 23, 9 patients)] or group B [DOC present (Glasgow Coma Scale <15 and CRS-R < 23, 20 patients)]. Voxel-wise statistical analysis of fractional anisotropy data was performed by using TBSS as implemented in the FMRIB Software Library. We calculated mean fractional anisotropy values across the white matter skeleton and within 48 regions of interest (ROIs) based on intersections between the skeleton and the probabilistic Johns Hopkins University white matter atlases. Among the 48 ROIs examined, the fractional anisotropy values of two ROIs (the left superior corona radiata, and left tapetum) were significantly lower in group B than in group A (P < 0.05). No significant differences were observed, however, in the other 46 ROIs (P > 0.05). Our results suggest that abnormalities of the superior corona radiata and tapetum may be critical for DOC presence in patients with HI-BI.

Probabilistic Presurgical Language fMRI Atlas of Patients with Brain Tumors.

Patients with brain tumors have high intersubject variation in putative language regions, which may limit the utility of straightforward application of healthy subject brain atlases in clinical scenarios. The purpose of this study was to develop a probabilistic functional brain atlas that consolidates language functional activations of sentence completion and Silent Word Generation language paradigms using a large sample of patients with brain tumors. The atlas was developed using retrospectively collected fMRI data from patients with brain tumors who underwent their first standard-of-care presurgical language fMRI scan at our institution between July 18, 2015, and May 13, 2022. Three hundred seventeen patients (861 fMRI scans) were used to develop the language functional atlas. An independent presurgical language fMRI data set of 39 patients with brain tumors from a previous study was used to evaluate our atlas. Family-wise error-corrected binary functional activation maps from sentence completion, letter fluency, and category fluency presurgical fMRI were used to create probability overlap maps and pooled probabilistic overlap maps in Montreal Neurological Institute standard space. The Wilcoxon signed-rank test was used to determine a significant difference in the maximum Dice coefficient for our atlas compared with a meta-analysis-based template with respect to expert-delineated primary language area activations. Probabilities of activating the left anterior primary language area and left posterior primary language area in the temporal lobe were 87.9% and 91.5%, respectively, for sentence completion, 88.5% and 74.2%, respectively, for letter fluency, and 83.6% and 67.6%, respectively, for category fluency. Maximum Dice coefficients for templates derived from our language atlas were significantly higher than the meta-analysis-based template in the left anterior primary language area (0.351 and 0.326, respectively, P < .05) and the left posterior primary language area in the temporal lobe (0.274 and 0.244, respectively, P < .005). Brain tumor patient- and paradigm-specific probabilistic language atlases were developed. These atlases had superior spatial agreement with fMRI activations in individual patients compared with the meta-analysis-based template.

MRI atlas of the pituitary gland in young female adults.

The probabilistic topography and inter-individual variability of the pituitary gland (PG) remain undetermined. The absence of a standardized reference atlas hinders research on PG volumetrics. In this study, we aimed at creating maximum probability maps for the anterior and posterior PG in young female adults. We manually delineated the anterior and posterior parts of the pituitary glands in 26 healthy subjects using high-resolution MRI T1 images. A three-step procedure and a cost function-masking approach were employed to optimize spatial normalization for the PG. We generated probabilistic atlases and maximum probability maps, which were subsequently coregistered back to the subjects' space and compared to manual delineations. Manual measurements led to a total pituitary volume of 705 ± 88mm³, with the anterior and posterior volumes measuring 614 ± 82mm³ and 91 ± 20mm³, respectively. The mean relative volume difference between manual and atlas-based estimations was 1.3%. The global pituitary atlas exhibited an 80% (± 9%) overlap for the DICE index and 67% (± 11%) for the Jaccard index. Similarly, these values were 77% (± 13%) and 64% (± 14%) for the anterior pituitary atlas and 62% (± 21%) and 47% (± 17%) for the posterior PG atlas, respectively. We observed a substantial concordance and a significant correlation between the volume estimations of the manual and atlas-based methods for the global pituitary and anterior volumes. The maximum probability maps of the anterior and posterior PG lay the groundwork for automatic atlas-based segmentation methods and the standardized analysis of large PG datasets.

Decoding Autism: Uncovering patterns in brain connectivity through sparsity analysis with rs-fMRI data

Background:In the realm of neuro-disorders, precise diagnosis and treatment rely heavily on objective imaging-based biomarker identification. This study employs a sparsity approach on resting-state fMRI to discern relevant brain region connectivity for predicting Autism. New method:The proposed methodology involves four key steps: (1) Utilizing three probabilistic brain atlases to extract functionally homogeneous brain regions from fMRI data. (2) Employing a hybrid approach of Graphical Lasso and Akaike Information Criteria to optimize sparse inverse covariance matrices for representing the brain functional connectivity. (3) Employing statistical techniques to scrutinize functional brain structures in Autism and Control subjects. (4) Implementing both autoencoder-based feature extraction and entire feature-based approach coupled with AI-based learning classifiers to predict Autism. Results:The ensemble classifier with the extracted feature set achieves a classification accuracy of 84.7% ± 0.3% using the MSDL atlas. Meanwhile, the 1D-CNN model, employing all features, exhibits superior classification accuracy of 88.6% ± 1.7% with the Smith 2009 (rsn70) atlas. Comparison with existing method (s):The proposed methodology outperforms the conventional correlation-based functional connectivity approach with a notably high prediction accuracy of more than 88%, whereas considering all direct and noisy indirect region-based functional connectivity, the traditional methods bound the prediction accuracy within 70% to 79%. Conclusions:This study underscores the potential of sparsity-based FC analysis using rs-fMRI data as a prognostic biomarker for detecting Autism.

Fibre orientation atlas guided rapid segmentation of white matter tracts.

Fibre tract delineation from diffusion magnetic resonance imaging (MRI) is a valuable clinical tool for neurosurgical planning and navigation, as well as in research neuroimaging pipelines. Several popular methods are used for this task, each with different strengths and weaknesses making them more or less suited to different contexts. For neurosurgical imaging, priorities include ease of use, computational efficiency, robustness to pathology and ability to generalise to new tracts of interest. Many existing methods use streamline tractography, which may require expert neuroimaging operators for setting parameters and delineating anatomical regions of interest, or suffer from as a lack of generalisability to clinical scans involving deforming tumours and other pathologies. More recently, data-driven approaches including deep-learning segmentation models and streamline clustering methods have improved reproducibility and automation, although they can require large amounts of training data and/or computationally intensive image processing at the point of application. We describe an atlas-based direct tract mapping technique called 'tractfinder', utilising tract-specific location and orientation priors. Our aim was to develop a clinically practical method avoiding streamline tractography at the point of application while utilising prior anatomical knowledge derived from only 10-20 training samples. Requiring few training samples allows emphasis to be placed on producing high quality, neuro-anatomically accurate training data, and enables rapid adaptation to new tracts of interest. Avoiding streamline tractography at the point of application reduces computational time, false positives and vulnerabilities to pathology such as tumour deformations or oedema. Carefully filtered training streamlines and track orientation distribution mapping are used to construct tract specific orientation and spatial probability atlases in standard space. Atlases are then transformed to target subject space using affine registration and compared with the subject's voxel-wise fibre orientation distribution data using a mathematical measure of distribution overlap, resulting in a map of the tract's likely spatial distribution. This work includes extensive performance evaluation and comparison with benchmark techniques, including streamline tractography and the deep-learning method TractSeg, in two publicly available healthy diffusion MRI datasets (from TractoInferno and the Human Connectome Project) in addition to a clinical dataset comprising paediatric and adult brain tumour scans. Tract segmentation results display high agreement with established techniques while requiring less than 3 min on average when applied to a new subject. Results also display higher robustness than compared methods when faced with clinical scans featuring brain tumours and resections. As well as describing and evaluating a novel proposed tract delineation technique, this work continues the discussion on the challenges surrounding the white matter segmentation task, including issues of anatomical definitions and the use of quantitative segmentation comparison metrics.

Biometry extraction and probabilistic anatomical atlas of the anterior Visual Pathway using dedicated high-resolution 3-D MRI

Anterior Visual Pathway (aVP) damage may be linked to diverse inflammatory, degenerative and/or vascular conditions. Currently however, a standardized methodological framework for extracting MRI biomarkers of the aVP is not available. We used high-resolution, 3-D MRI data to generate a probabilistic anatomical atlas of the normal aVP and its intraorbital (iOrb), intracanalicular (iCan), intracranial (iCran), optic chiasm (OC), and tract (OT) subdivisions. We acquired 0.6 mm3 steady-state free-precession images from 24 healthy participants using a 3 T scanner. aVP masks were obtained by manual segmentation of each aVP subdivision. Mask straightening and normalization with cross-sectional area (CSA) preservation were obtained using scripts developed in-house. A probabilistic atlas (“aVP-24”) was generated by averaging left and right sides of all subjects. Leave-one-out cross-validation with respect to interindividual variability was performed employing the Dice Similarity Index (DSI). Spatially normalized representations of the aVP subdivisions were generated. Overlapping CSA values before and after normalization demonstrate preservation of the aVP cross-section. Volume, length, CSA, and ellipticity index (ε) biometrics were extracted. The aVP-24 morphology followed previous descriptions from the gross anatomy. Atlas spatial validation DSI scores of 0.85 in 50% and 0.77 in 95% of participants indicated good generalizability across the subjects. The proposed MRI standardization framework allows for previously unavailable, geometrically unbiased biometric data of the entire aVP and provides the base for future spatial-resolved, group-level investigations.

Alzheimer Disease Pathology and Neurodegeneration in Midlife Obesity: A Pilot Study.

Obesity and excess adiposity at midlife are risk factors for Alzheimer disease (AD). Visceral fat is known to be associated with insulin resistance and a pro-inflammatory state, the two mechanisms involved in AD pathology. We assessed the association of obesity, MRI-determined abdominal adipose tissue volumes, and insulin resistance with PET-determined amyloid and tau uptake in default mode network areas, and MRI-determined brain volume and cortical thickness in AD cortical signature in the cognitively normal midlife population. Thirty-two middle-aged (age: 51.27±6.12 years, 15 males, body mass index (BMI): 32.28±6.39 kg/m2) cognitively normal participants, underwent bloodwork, brain and abdominal MRI, and amyloid and tau PET scan. Visceral and subcutaneous adipose tissue (VAT, SAT) were semi-automatically segmented using VOXel Analysis Suite (Voxa). FreeSurfer was used to automatically segment brain regions using a probabilistic atlas. PET scans were acquired using [11C]PiB and AV-1451 tracers and were analyzed using PET unified pipeline. The association of brain volumes, cortical thicknesses, and PiB and AV-1451 standardized uptake value ratios (SUVRs) with BMI, VAT/SAT ratio, and insulin resistance were assessed using Spearman's partial correlation. VAT/SAT ratio was associated significantly with PiB SUVRs in the right precuneus cortex (p=0.034) overall, controlling for sex. This association was significant only in males (p=0.044), not females (p=0.166). Higher VAT/SAT ratio and PiB SUVRs in the right precuneus cortex were associated with lower cortical thickness in AD-signature areas predominantly including bilateral temporal cortices, parahippocampal, medial orbitofrontal, and cingulate cortices, with age and sex as covariates. Also, higher BMI and insulin resistance were associated with lower cortical thickness in bilateral temporal poles. In midlife cognitively normal adults, we demonstrated higher amyloid pathology in the right precuneus cortex in individuals with a higher VAT/SAT ratio, a marker of visceral obesity, along with a lower cortical thickness in AD-signature areas associated with higher visceral obesity, insulin resistance, and amyloid pathology.

Association between basal forebrain and medial temporal lobe tau, and gray matter volume

AbstractBackgroundTau pathology is a hallmark of Alzheimer’s disease (AD). The basal forebrain (BF) is a significant cholinergic projection site with cholinergic groups 1‐3 (Ch1‐3) projecting to the hippocampus, hypothalamus, and olfactory bulb, and group 4 (Ch4) projecting to the cortical mantle and amygdala. Braak stage 1 includes BF tau and recent literature suggests BF degeneration precedes medial temporal lobe (MTL) degeneration, thus implicating early involvement of the BF in AD. This study examines BF 18F‐flortaucipir (FTP) retention and gray matter (GM) volume, and their associations with MTL FTP retention and GM volume.MethodData were acquired for 111 older adults (Table 1) in the Baltimore Longitudinal Study of Aging. Binary masks for BF subregions Ch1‐3 and Ch4 were generated using a probabilistic atlas (Figure 1). We computed mean standardized uptake value ratio (SUVR) using an inferior cerebellar GM reference region and mean GM volume using average voxel‐based morphometry values for each BF subregion. Colocalized relationships between BF tau and volume were assessed using Pearson correlations. Proximal relationships between BF tau/GM and MTL tau/GM were assessed at the voxel level controlling for age, sex, and intracranial volume and corrected for multiple comparisons using non‐parametric permutations.ResultsWithin the BF, FTP‐SUVR in Ch1‐3 was strongly related to FTP‐SUVR in Ch4, and FTP‐SUVR was associated with larger volume in Ch1‐3, but not Ch4 (Figure 2). Voxelwise analyses showed strong BF‐MTL associations with higher Ch1‐3 and Ch4 FTP‐SUVR associated with higher FTP‐SUVR throughout the MTL. The strongest associations were observed anteriorly (amygdala, peri/entorhinal cortices) and medially along the longitudinal axis of the hippocampus/parahippocampal gyrus. Moreover, for both BF Ch groups, larger volume was associated with larger MTL volume. This association was strongest in the amygdala and hippocampal head and body.ConclusionAs expected, BF FTP‐SUVR was positively associated with MTL FTP‐SUVR, and BF volume was positively associated with MTL GM volume, supporting the use of these BF measures. Further analysis is necessary to better understand the positive associations between BF FTP‐SUVR and GM volume and to assess the temporal ordering of FTP‐SUVR accumulation in the BF and MTL.

Visceral Abdominal Adipose Tissue and Insulin Resistance Respectively Influence Alzheimer Disease Amyloid Pathology and Neurodegeneration in Midlife

AbstractBackgroundObesity and adiposity at midlife, evidenced by high body mass index (BMI), are increasingly understood as a risk factor for Alzheimer’s disease (AD). Importantly, visceral fat is known to be associated with insulin resistance and proinflammatory state, the mechanisms involved in AD pathology. Herein, we aimed to assess the association between brain MRI volumes as well as amyloid and tau uptake with obesity, insulin resistance, and abdominal adipose tissue in cognitively normal midlife population.MethodA total of 34 middle‐aged (age: 51.27 ± 6.12 years, BMI: 32.28 ± 6.39 kg/m2), cognitively normal participants, underwent bloodwork, brain and abdominal MRI, as well as amyloid and tau PET scan. Homeostatic Model Assessment for Insulin Resistance (HOMAIR) &gt; 1.9 was used as a measure of insulin resistance. Visceral and subcutaneous adipose tissue (VAT, SAT) were semi‐automatically segmented using VOXel Analysis Suite (Voxa). FreeSurfer 7.1.1 was used for automatic segmentation of cortical and subcortical brain regions using a probabilistic atlas. Dynamic amyloid imaging was performed with a bolus injection of ∼15 mCi of [11C]PiB, followed by a 60‐min scan. A single intravenous bolus of between 7.2‐10.8 mCi of AV‐1451 was administered. Data from the 30‐60 minute, and 80‐100 minute post‐injection window for PiB and AV‐1451 were used for the analysis, respectively. The association of brain volumes and PiB and AV‐1451 SUVRs within the default mode network areas with BMI and VAT/SAT ratio were assessed using linear regression models.ResultWe observed lower right entorhinal white matter volumes in obese participants with insulin resistance compared to metabolically normal non‐obese group (p = 0.004), without any significant difference in PiB or AV‐1451 SUVRs. Regression models with sex, age and education as covariates showed a significant positive association between VAT/SAT ratio and left precuneus white matter PiB SUVRs (R2 = 0.31, p = 0.005), but no significant associations with AV‐1451 SUVRs.ConclusionIn our midlife obese sample with insulin resistance, there were lower right entorhinal white matter volume, which is involved in relaying information to the hippocampus. We also demonstrate higher early amyloid pathology in AD‐signature areas such as the precuneus in mid‐life persons with high VAT/SAT ratio, a marker of visceral obesity.

Manual segmentation of the paraventricular nucleus of the hypothalamus and the dorsal and ventral bed nucleus of stria terminalis using multimodal 7 Tesla structural MRI: probabilistic atlases for a stress-control triad

The paraventricular nucleus of the hypothalamus (PVN) is uniquely capable of proximal control over autonomic and neuroendocrine stress responses, and the bed nucleus of the stria terminalis (BNST) directly modulates PVN function, as well as playing an important role in stress control itself. The dorsal BNST (dBNST) is predominantly preautonomic, while the ventral BNST (vBNST) is predominantly viscerosensory, receiving dense noradrenergic signaling. Distinguishing the dBNST and vBNST, along with the PVN, may facilitate our understanding of dynamic interactions among these regions. T1-weighted MPRAGE and high resolution gradient echo (GRE) modalities were acquired at 7T. GRE was coregistered to MPRAGE and segmentations were performed in MRIcroGL based on their Atlas of the Human Brain depictions. The dBNST, vBNST and PVN were manually segmented in 25 participants; 10 images were rated by 2 raters. These segmentations were normalized and probabilistic atlases for each region were generated in MNI space, now available as resources for future research. We found moderate–high inter-rater reliability [n = 10; Mean Dice (SD); PVN = 0.69 (0.04); dBNST = 0.77 (0.04); vBNST = 0.62 (0.04)]. Probabilistic atlases were reverse normalized into native space for six additional participants that were segmented but not included in the original 25. We also found moderate to moderate–high reliability between the probabilistic atlases and manual segmentations [n = 6; Mean Dice (SD); PVN = 0.55 (0.12); dBNST = 0.60 (0.10); vBNST = 0.47 (0.12 SD)]. By isolating these hypothalamic and BNST subregions using ultra-high field MRI modalities, more specific delineations of these regions can facilitate greater understanding of mechanisms underlying stress-related function and psychopathology.

Multi-object 3D segmentation of brain structures using a geometric deformable model with a priori knowledge

ABSTRACT Brain structure segmentation in 3D Magnetic Resonance Images is crucial for understanding neurodegenerative disorders. Manual segmentation is error-prone, necessitating robust automated techniques. In this paper, we introduce a novel and robust approach for the simultaneous segmentation of multiple brain structures in MRI images. Our method involves the concurrent evolution of 3D surfaces toward predefined anatomical targets, employing an efficient multi-object generalized fast marching method (MOGFMM) for simultaneous object detection. Additionally, we propose an effective evolution function that integrates prior knowledge from anatomical and probabilistic atlases, as well as spatial relationships among the segmented structures. Each deformable surface corresponds to a specific structure. To validate our approach, we conducted experiments on a dataset of real brain images (IBSR) and compared the results with several state-of-the-art methods. The obtained results were promising, demonstrating the effectiveness and superiority of our developed method.

Accurate Bayesian segmentation of thalamic nuclei using diffusion MRI and an improved histological atlas

The human thalamus is a highly connected brain structure, which is key for the control of numerous functions and is involved in several neurological disorders. Recently, neuroimaging studies have increasingly focused on the volume and connectivity of the specific nuclei comprising this structure, rather than looking at the thalamus as a whole. However, accurate identification of cytoarchitectonically designed histological nuclei on standard in vivo structural MRI is hampered by the lack of image contrast that can be used to distinguish nuclei from each other and from surrounding white matter tracts. While diffusion MRI may offer such contrast, it has lower resolution and lacks some boundaries visible in structural imaging. In this work, we present a Bayesian segmentation algorithm for the thalamus. This algorithm combines prior information from a probabilistic atlas with likelihood models for both structural and diffusion MRI, allowing segmentation of 25 thalamic labels per hemisphere informed by both modalities. We present an improved probabilistic atlas, incorporating thalamic nuclei identified from histology and 45 white matter tracts surrounding the thalamus identified in ultra-high gradient strength diffusion imaging. We present a family of likelihood models for diffusion tensor imaging, ensuring compatibility with the vast majority of neuroimaging datasets that include diffusion MRI data. The use of these diffusion likelihood models greatly improves identification of nuclear groups versus segmentation based solely on structural MRI. Dice comparison of 5 manually identifiable groups of nuclei to ground truth segmentations show improvements of up to 10 percentage points. Additionally, our chosen model shows a high degree of reliability, with median test-retest Dice scores above 0.85 for four out of five nuclei groups, whilst also offering improved detection of differential thalamic involvement in Alzheimer’s disease (AUROC 81.98%). The probabilistic atlas and segmentation tool will be made publicly available as part of the neuroimaging package FreeSurfer (https://freesurfer.net/fswiki/ThalamicNucleiDTI).

Non-literal language processing is jointly supported by the language and theory of mind networks: Evidence from a novel meta-analytic fMRI approach

Going beyond the literal meaning of language is key to communicative success. However, the mechanisms that support non-literal inferences remain debated. Using a novel meta-analytic approach, we evaluate the contribution of linguistic, social-cognitive, and executive mechanisms to non-literal interpretation. We identified 74 fMRI experiments (n = 1,430 participants) from 2001 to 2021 that contrasted non-literal language comprehension with a literal control condition, spanning ten phenomena (e.g., metaphor, irony, indirect speech). Applying the activation likelihood estimation approach to the 825 activation peaks yielded six left-lateralized clusters. We then evaluated the locations of both the individual-study peaks and the clusters against probabilistic functional atlases (cf. anatomical locations, as is typically done) for three candidate brain networks—the language-selective network (Fedorenko, Behr, & Kanwisher, 2011), which supports language processing, the Theory of Mind (ToM) network (Saxe & Kanwisher, 2003), which supports social inferences, and the domain-general Multiple-Demand (MD) network (Duncan, 2010), which supports executive control. These atlases were created by overlaying individual activation maps of participants who performed robust and extensively validated ‘localizer’ tasks that selectively target each network in question (n = 806 for language; n = 198 for ToM; n = 691 for MD). We found that both the individual-study peaks and the ALE clusters fell primarily within the language network and the ToM network. These results suggest that non-literal processing is supported by both i) mechanisms that process literal linguistic meaning, and ii) mechanisms that support general social inference. They thus undermine a strong divide between literal and non-literal aspects of language and challenge the claim that non-literal processing requires additional executive resources.

In Reply: Planning Brain Tumor Resection Using a Probabilistic Atlas of Cortical and Subcortical Structures Critical for Functional Processing: A Proof of Concept.

In Reply: Planning Brain Tumor Resection Using a Probabilistic Atlas of Cortical and Subcortical Structures Critical for Functional Processing: A Proof of Concept.

Letter: Planning Brain Tumor Resection Using a Probabilistic Atlas of Cortical and Subcortical Structures Critical for Functional Processing: A Proof of Concept.

Letter: Planning Brain Tumor Resection Using a Probabilistic Atlas of Cortical and Subcortical Structures Critical for Functional Processing: A Proof of Concept.

Inconsistencies in atlas-based volumetric measures of the human nucleus basalis of Meynert: A need for high-resolution alternatives

The nucleus basalis of Meynert (nbM) is the major source of cortical acetylcholine (ACh) and has been related to cognitive processes and to neurological disorders. However, spatially delineating the human nbM in MRI studies remains challenging. Due to the absence of a functional localiser for the human nbM, studies to date have localised it using nearby neuroanatomical landmarks or using probabilistic atlases. To understand the feasibility of MRI of the nbM we set our four goals; our first goal was to review current human nbM region-of-interest (ROI) selection protocols used in MRI studies, which we found have reported highly variable nbM volume estimates. Our next goal was to quantify and discuss the limitations of existing atlas-based volumetry of nbM. We found that the identified ROI volume depends heavily on the atlas used and on the probabilistic threshold set. In addition, we found large disparities even for data/studies using the same atlas and threshold. To test whether spatial resolution contributes to volume variability, as our third goal, we developed a novel nbM mask based on the normalized BigBrain dataset. We found that as long as the spatial resolution of the target data was 1.3 mm isotropic or above, our novel nbM mask offered realistic and stable volume estimates. Finally, as our last goal we tried to discern nbM using publicly available and novel high resolution structural MRI ex vivo MRI datasets. We find that, using an optimised 9.4T quantitative T2⁎ ex vivo dataset, the nbM can be visualised using MRI. We conclude caution is needed when applying the current methods of mapping nbM, especially for high resolution MRI data. Direct imaging of the nbM appears feasible and would eliminate the problems we identify, although further development is required to allow such imaging using standard (f)MRI scanning.

Neural activity of the auditory cortex predicts speech recognition of patients with asymmetric hearing loss after cochlear implantation

Patients with asymmetric hearing loss show an asymmetry of glucose metabolism of the primary auditory cortex (PAC). We investigated whether this asymmetry could serve as an objective predictor for speech recognition with CI. Nine patients underwent 18FDG PET prior to CI surgery. Average normalized 18FDG uptake of 25% of voxels with highest uptake was calculated for the PAC employing a probabilistic atlas and cerebellar cortex as reference. Differences in glucose metabolism of the PAC were assessed by an asymmetry index (AI-PAC). We tested the correlation between outcome of CI surgery (6 months post implantation), AI-PAC and clinical predictors. Pre-operative AI-PAC showed a positive correlation with speech recognition with CI (significant for sentences and numbers; trend for monosyllabic words). With a pre-operative AI-PAC ≥ 4.2%, patients reached good CI outcome in sentence recognition of 59–90% and number recognition of 90–100% and less favorable CI outcome in monosyllabic word recognition of 25–45%. Age at symptom onset was significantly associated with all measures of speech recognition, while deafness duration was only associated with sentence recognition. AI-PAC allows for a reliable and quantitative pre-operative prediction of early improvement in speech recognition after CI. 18FDG PET may be a valuable addition to the objective pre-operative assessment of CI candidates. Further studies in larger cohorts and with longer follow-up times are needed.

Changes in subcortical white matter in the unaffected hemisphere following unilateral spontaneous intracerebral hemorrhage: a tract-based spatial statistics study.

We investigated changes in the subcortical white matter in the unaffected hemisphere in patients with unilateral intracerebral hemorrhage (ICH) by applying tract-based spatial statistics (TBSS) analysis. Twenty-four patients with ICH and 17 healthy control subjects were recruited for this study. Diffusion tensor imaging (DTI) data were obtained at least four weeks after ICH onset. TBSS analysis was performed using fractional anisotropy (FA) DTI data. We calculated mean FA values across the tract skeleton and within 27 regions of interest (ROIs) based on the observed intersections between the FA skeleton and the probabilistic Johns Hopkins University white matter atlases. The FA values of 27 ROIs in the unaffected hemisphere in the patient group were significantly lower than those of the control group (p < 0.05). In terms of a causal relationship between possible confounding factors (sex, age, lateralization [right], lesion volume), a negative correlation coefficient was observed in five ROIs (the tapetum, sagittal stratum, column and body of the fornix, posterior corona radiate, inferior cerebellar peduncle, superior cerebellar peduncle) in the regression analysis (p < 0.05). In the patient group, moderate negative correlations were detected between ICH volume and the FA values of two ROIs: the sagittal stratum, r = -0.479, p < 0.05; the tapetum, r = -0.414, p < 0.05. We detected extensive neural injury of the subcortical white matter in the unaffected hemisphere in patients with unilateral ICH. In addition, injury severities of neural structures located around the mid-sagittal line or periventricular areas were correlated with ICH volume.

A genuine interindividual variability in number and anatomical localization of face-selective regions in the human brain.

Neuroimaging studies have reported regions with more neural activation to face than nonface stimuli in the human occipitotemporal cortex for three decades. Here we used a highly sensitive and reliable frequency-tagging functional magnetic resonance imaging paradigm measuring high-level face-selective neural activity to assess interindividual variability in the localization and number of face-selective clusters. Although the majority of these clusters are located in the same cortical gyri and sulci across 25 adult brains, a volume-based analysis of unsmoothed data reveals a large amount of interindividual variability in their spatial distribution and number, particularly in the ventral occipitotemporal cortex. In contrast to the widely held assumption, these face-selective clusters cannot be objectively related on a one-to-one basis across individual brains, do not correspond to a single cytoarchitectonic region, and are not clearly demarcated by estimated posteroanterior cytoarchitectonic borders. Interindividual variability in localization and number of cortical face-selective clusters does not appear to be due to the measurement noise but seems to be genuine, casting doubt on definite labeling and interindividual correspondence of face-selective "areas" and questioning their a priori definition based on cytoarchitectony or probabilistic atlases of independent datasets. These observations challenge conventional models of human face recognition based on a fixed number of discrete neurofunctional information processing stages.

Joint-label fusion brain atlases for dementia research in Down syndrome.

Research suggests a link between Alzheimer's Disease in Down Syndrome (DS) and the overproduction of amyloid plaques. Using Positron Emission Tomography (PET) we can assess the in‐vivo regional amyloid load using several available ligands. To measure amyloid distributions in specific brain regions, a brain atlas is used. A popular method of creating a brain atlas is to segment a participant's structural Magnetic Resonance Imaging (MRI) scan. Acquiring an MRI is often challenging in intellectually‐imparied populations because of contraindications or data exclusion due to significant motion artifacts or incomplete sequences related to general discomfort. When an MRI cannot be acquired, it is typically replaced with a standardized brain atlas derived from neurotypical populations (i.e. healthy individuals without DS) which may be inappropriate for use in DS. In this project, we create a series of disease and diagnosis‐specific (cognitively stable (CS‐DS), mild cognitive impairment (MCI‐DS), and dementia (DEM‐DS)) probabilistic group atlases of participants with DS and evaluate their accuracy of quantifying regional amyloid load compared to the individually‐based MRI segmentations. Further, we compare the diagnostic‐specific atlases with a probabilistic atlas constructed from similar‐aged cognitively‐stable neurotypical participants. We hypothesized that regional PET signals will best match the individually‐based MRI segmentations by using DS group atlases that aligns with a participant's disorder and disease status (e.g. DS and MCI‐DS). Our results vary by brain region but generally show that using a disorder‐specific atlas in DS better matches the individually‐based MRI segmentations than using an atlas constructed from cognitively‐stable neurotypical participants. We found no additional benefit of using diagnose‐specific atlases matching disease status. All atlases are made publicly available for the research community.Highlight Down syndrome (DS) joint‐label‐fusion atlases provide accurate positron emission tomography (PET) amyloid measurements.A disorder‐specific DS atlas is better than a neurotypical atlas for PET quantification.It is not necessary to use a disease‐state–specific atlas for quantification in aged DS.Dorsal striatum results vary, possibly due to this region and dementia progression.