Statistical neuroanatomy of the human inferior frontal gyrus and probabilistic atlas in a standard stereotaxic space

Unraveling glioblastoma diversity: Insights into methylation subtypes and spatial relationships

Background

The purpose of this study was to elucidate the relationship between distinct brain regions and molecular subtypes in glioblastoma (GB), focusing on integrating modern statistical tools and molecular profiling to better understand the heterogeneity of Isocitrate Dehydrogenase wild-type (IDH-wt) gliomas.

Methods

This retrospective study comprised 441 patients diagnosed with new IDH-wt glioma between 2009 and 2020 at Heidelberg University Hospital. The diagnostic process included preoperative magnetic resonance imaging and molecular characterization, encompassing IDH-status determination and subclassification, through DNA-methylation profiling. To discern and map distinct brain regions associated with specific methylation subtypes, a support-vector regression-based lesion-symptom mapping (SVR-LSM) was employed. Lesion maps were adjusted to 2 mm³ resolution. Significance was assessed with beta maps, using a threshold of P < .005, with 10 000 permutations and a cluster size minimum of 100 voxels.

Results

Of 441 initially screened glioma patients, 423 (95.9%) met the inclusion criteria. Following DNA-methylation profiling, patients were classified into RTK II (40.7%), MES (33.8%), RTK I (18%), and other methylation subclasses (7.6%). Between molecular subtypes, there was no difference in tumor volume. Using SVR-LSM, distinct brain regions correlated with each subclass were identified: MES subtypes were associated with left-hemispheric regions involving the superior temporal gyrus and insula cortex, RTK I with right frontal regions, and RTK II with 3 clusters in the left hemisphere.

Conclusions

This study linked molecular diversity and spatial features in glioblastomas using SVR-LSM. Future studies should validate these findings in larger, independent cohorts to confirm the observed patterns.

Evidence for inflammation in normal-appearing brain regions in patients with growing sporadic vestibular schwannoma: A PET study

Background

Nonauditory symptoms can be a prominent feature in patients with sporadic vestibular schwannoma (VS), but the cause of these symptoms is unknown. Inflammation is hypothesized to play a key role in the growth and symptomatic presentation of sporadic VS, and in this study, we investigated through translocator protein (TSPO) positron emission tomography (PET) whether inflammation occurred within the "normal appearing" brain of such patients and its association with tumor growth.

Methods

Dynamic PET datasets from 15 patients with sporadic VS (8 static and 7 growing) who had been previously imaged using the TSPO tracer [11C](R)-PK11195 were included. Parametric images of [11C](R)-PK11195 binding potential (BPND) and the distribution volume ratio (DVR) were derived and compared across VS growth groups within both contralateral and ipsilateral gray (GM) and white matter (WM) regions. Voxel-wise cluster analysis was additionally performed to identify anatomical regions of increased [11C](R)-PK11195 binding.

Results

Compared with static tumors, growing VS demonstrated significantly higher cortical (GM, 1.070 vs. 1.031, P = .03) and whole brain (GM & WM, 1.045 vs. 1.006, P = .03) [11C](R)-PK11195 DVR values. The voxel-wise analysis supported the region-based analysis and revealed clusters of high TSPO binding within the precentral, postcentral, and prefrontal cortex in patients with growing VS.

Conclusions

We present the first in vivo evidence of increased TSPO expression and inflammation within the brains of patients with growing sporadic VS. These results provide a potential mechanistic insight into the development of nonauditory symptoms in these patients and highlight the need for further studies interrogating the role of neuroinflammation in driving VS symptomatology.

In humans, striato-pallido-thalamic projections are largely segregated by their origin in either the striosome-like or matrix-like compartments

Cortico-striato-thalamo-cortical (CSTC) loops are fundamental organizing units in mammalian brains. CSTCs process limbic, associative, and sensorimotor information in largely separated but interacting networks. CTSC loops pass through paired striatal compartments, striosome (aka patch) and matrix, segregated pools of medium spiny projection neurons with distinct embryologic origins, cortical/subcortical structural connectivity, susceptibility to injury, and roles in behaviors and diseases. Similarly, striatal dopamine modulates activity in striosome and matrix in opposite directions. Routing CSTCs through one compartment may be an anatomical basis for regulating discrete functions. We used differential structural connectivity, identified through probabilistic diffusion tractography, to distinguish the striatal compartments (striosome-like and matrix-like voxels) in living humans. We then mapped compartment-specific projections and quantified structural connectivity between each striatal compartment, the globus pallidus interna (GPi), and 20 thalamic nuclei in 221 healthy adults. We found that striosome-originating and matrix-originating streamlines were segregated within the GPi: striosome-like connectivity was significantly more rostral, ventral, and medial. Striato-pallido-thalamic streamline bundles that were seeded from striosome-like and matrix-like voxels transited spatially distinct portions of the white matter. Matrix-like streamlines were 5.7-fold more likely to reach the GPi, replicating animal tract-tracing studies. Striosome-like connectivity dominated in six thalamic nuclei (anteroventral, central lateral, laterodorsal, lateral posterior, mediodorsal-medial, and medial geniculate). Matrix-like connectivity dominated in seven thalamic nuclei (centromedian, parafascicular, pulvinar-anterior, pulvinar-lateral, ventral lateral-anterior, ventral lateral-posterior, ventral posterolateral). Though we mapped all thalamic nuclei independently, functionally-related nuclei were matched for compartment-level bias. We validated these results with prior thalamostriate tract tracing studies in non-human primates and other species; where reliable data was available, all agreed with our measures of structural connectivity. Matrix-like connectivity was lateralized (left > right hemisphere) in 18 thalamic nuclei, independent of handedness, diffusion protocol, sex, or whether the nucleus was striosome-dominated or matrix-dominated. Compartment-specific biases in striato-pallido-thalamic structural connectivity suggest that routing CSTC loops through striosome-like or matrix-like voxels is a fundamental mechanism for organizing and regulating brain networks. Our MRI-based assessments of striato-thalamic connectivity in humans match and extend the results of prior tract tracing studies in animals. Compartment-level characterization may improve localization of human neuropathologies and improve neurosurgical targeting in the GPi and thalamus.

Metabolic patterns in brain 18F-fluorodeoxyglucose PET relate to aetiology in paediatric dystonia

There is a lack of imaging markers revealing the functional characteristics of different brain regions in paediatric dystonia. In this observational study, we assessed the utility of [18F]2-fluoro-2-deoxy-D-glucose (FDG)-PET in understanding dystonia pathophysiology by revealing specific resting awake brain glucose metabolism patterns in different childhood dystonia subgroups. PET scans from 267 children with dystonia being evaluated for possible deep brain stimulation surgery between September 2007 and February 2018 at Evelina London Children's Hospital (ELCH), UK, were examined. Scans without gross anatomical abnormality (e.g. large cysts, significant ventriculomegaly; n = 240) were analysed with Statistical Parametric Mapping (SPM12). Glucose metabolism patterns were examined in the 144/240 (60%) cases with the 10 commonest childhood-onset dystonias, focusing on nine anatomical regions. A group of 39 adult controls was used for comparisons. The genetic dystonias were associated with the following genes: TOR1A, THAP1, SGCE, KMT2B, HPRT1 (Lesch Nyhan disease), PANK2 and GCDH (Glutaric Aciduria type 1). The acquired cerebral palsy (CP) cases were divided into those related to prematurity (CP-Preterm), neonatal jaundice/kernicterus (CP-Kernicterus) and hypoxic-ischaemic encephalopathy (CP-Term). Each dystonia subgroup had distinct patterns of altered FDG-PET uptake. Focal glucose hypometabolism of the pallidi, putamina or both, was the commonest finding, except in PANK2, where basal ganglia metabolism appeared normal. HPRT1 uniquely showed glucose hypometabolism across all nine cerebral regions. Temporal lobe glucose hypometabolism was found in KMT2B, HPRT1 and CP-Kernicterus. Frontal lobe hypometabolism was found in SGCE, HPRT1 and PANK2. Thalamic and brainstem hypometabolism were seen only in HPRT1, CP-Preterm and CP-term dystonia cases. The combination of frontal and parietal lobe hypermetabolism was uniquely found in CP-term cases. PANK2 cases showed a distinct combination of parietal hypermetabolism with cerebellar hypometabolism but intact putaminal-pallidal glucose metabolism. HPRT1, PANK2, CP-kernicterus and CP-preterm cases had cerebellar and insula glucose hypometabolism as well as parietal glucose hypermetabolism. The study findings offer insights into the pathophysiology of dystonia and support the network theory for dystonia pathogenesis. 'Signature' patterns for each dystonia subgroup could be a useful biomarker to guide differential diagnosis and inform personalized management strategies.

Evaluation of in vivo staging of amyloid deposition in cognitively unimpaired elderly aged 78-94

Amyloid-beta (Aβ) deposition is common in cognitively unimpaired (CU) elderly >85 years. This study investigated amyloid distribution and evaluated three published in vivo amyloid-PET staging schemes from a cognitively unimpaired (CU) cohort aged 84.9 ± 4.3 years (n = 75). SUV-based principal component analysis (PCA) was applied to 18F-flutemetamol PET data to determine an unbiased regional covariance pattern of tracer uptake across grey matter regions. PET staging schemes were applied to the data and compared to the PCA output. Concentration of p-tau181 was measured in blood plasma. The PCA revealed three distinct components accounting for 91.2% of total SUV variance. PC1 driven by the large common variance of uptake in neocortical and striatal regions was significantly positively correlated with global SUVRs, APOE4 status and p-tau181 concentration. PC2 represented mainly non-specific uptake in typical amyloid-PET reference regions, and PC3 the occipital lobe. Application of the staging schemes demonstrated that the majority of the CU cohort (up to 93%) were classified as having pathological amount and distribution of Aβ. Good correspondence existed between binary (+/-) classification and later amyloid stages, however, substantial differences existed between schemes for low stages with 8-17% of individuals being unstageable, i.e., not following the sequential progression of Aβ deposition. In spite of the difference in staging outcomes there was broad spatial overlap between earlier stages and PC1, most prominently in default mode network regions. This study critically evaluated the utility of in vivo amyloid staging from a single PET scan in CU elderly and found that early amyloid stages could not be consistently classified. The majority of the cohort had pathological Aβ, thus, it remains an open topic what constitutes abnormal brain Aβ in the oldest-old and what is the best method to determine that.

Research on Optimization Scheme for Blocking Artifacts after Patch-Based Medical Image Reconstruction

Due to limitations of computer resources, when utilizing a neural network to process an image with a high resolution, the typical processing approach is to slice the original image. However, because of the influence of zero-padding in the edge component during the convolution process, the central part of the patch often has more accurate feature information than the edge part, resulting in image blocking artifacts after patch stitching. We studied this problem in this paper and proposed a fusion method that assigns a weight to each pixel in a patch using a truncated Gaussian function as the weighting function. In this method, we used the weighting function to transform the Euclidean-distance between a point in the overlapping part and the central point of the patch where the point was located into a weight coefficient. With increasing distance, the value of the weight coefficient decreased. Finally, the reconstructed image was obtained by weighting. We employed the bias correction model to evaluate our method on the simulated database BrainWeb and the real dataset HCP (Human Connectome Project). The results show that the proposed method is capable of effectively removing blocking artifacts and obtaining a smoother bias field. To verify the effectiveness of our algorithm, we employed a denoising model to test it on the IXI-Guys human dataset. Qualitative and quantitative evaluations of both models show that the fusion method proposed in this paper can effectively remove blocking artifacts and demonstrates superior performance compared to five commonly available and state-of-the-art fusion methods.

Spatial normalization and quantification approaches of PET imaging for neurological disorders

Quantification approaches of positron emission tomography (PET) imaging provide user-independent evaluation of pathophysiological processes in living brains, which have been strongly recommended in clinical diagnosis of neurological disorders. Most PET quantification approaches depend on spatial normalization of PET images to brain template; however, the spatial normalization and quantification approaches have not been comprehensively reviewed. In this review, we introduced and compared PET template-based and magnetic resonance imaging (MRI)-aided spatial normalization approaches. Tracer-specific and age-specific PET brain templates were surveyed between 1999 and 2021 for ¹⁸F-FDG, ¹¹C-PIB, ¹⁸F-Florbetapir, ¹⁸F-THK5317, and etc., as well as adaptive PET template methods. Spatial normalization-based PET quantification approaches were reviewed, including region-of-interest (ROI)-based and voxel-wise quantitative methods. Spatial normalization-based ROI segmentation approaches were introduced, including manual delineation on template, atlas-based segmentation, and multi-atlas approach. Voxel-wise quantification approaches were reviewed, including voxel-wise statistics and principal component analysis. Certain concerns and representative examples of clinical applications were provided for both ROI-based and voxel-wise quantification approaches. At last, a recipe for PET spatial normalization and quantification approaches was concluded to improve diagnosis accuracy of neurological disorders in clinical practice.

Audio and visual speech emotion activate the left pre-supplementary motor area

Sensorimotor brain areas have been implicated in the recognition of emotion expressed on the face and through nonverbal vocalizations. However, no previous study has assessed whether sensorimotor cortices are recruited during the perception of emotion in speech-a signal that includes both audio (speech sounds) and visual (facial speech movements) components. To address this gap in the literature, we recruited 24 participants to listen to speech clips produced in a way that was either happy, sad, or neutral in expression. These stimuli also were presented in one of three modalities: audio-only (hearing the voice but not seeing the face), video-only (seeing the face but not hearing the voice), or audiovisual. Brain activity was recorded using electroencephalography, subjected to independent component analysis, and source-localized. We found that the left presupplementary motor area was more active in response to happy and sad stimuli than neutral stimuli, as indexed by greater mu event-related desynchronization. This effect did not differ by the sensory modality of the stimuli. Activity levels in other sensorimotor brain areas did not differ by emotion, although they were greatest in response to visual-only and audiovisual stimuli. One possible explanation for the pre-SMA result is that this brain area may actively support speech emotion recognition by using our extensive experience expressing emotion to generate sensory predictions that in turn guide our perception.

Broca's area and the search for anatomical asymmetry: commentary and perspectives

We present a brief commentary on the field's search for an anatomical asymmetry between Broca's area and its homologue in the non-dominant hemisphere, focusing on a selection of studies, including research from the last decade. We demonstrate that, several years after the influential review of Keller and colleagues from 2009, and despite recent advances in neuroimaging, the existence of a structural asymmetry of Broca's area is still controversial. This is especially the case for studies of the macroanatomy of this region. We point out the inconsistencies in methodology across studies that could account for the discrepancy in results. Investigations of the microstructure of Broca's area show a trend of a leftward asymmetry, but it is still unclear how these results relate to language dominance. We suggest that it may be necessary to combine multiple metrics in a systematic manner to find robust asymmetries and to expand the regional scope of structural investigations. Finally, based on the current state of the literature, we should not rule out the possibility that language dominance may simply not be reflected in local anatomical differences in the brain.

Denoising of 3D Brain MR Images with Parallel Residual Learning of Convolutional Neural Network Using Global and Local Feature Extraction

Magnetic resonance (MR) images often suffer from random noise pollution during image acquisition and transmission, which impairs disease diagnosis by doctors or automated systems. In recent years, many noise removal algorithms with impressive performances have been proposed. In this work, inspired by the idea of deep learning, we propose a denoising method named 3D-Parallel-RicianNet, which will combine global and local information to remove noise in MR images. Specifically, we introduce a powerful dilated convolution residual (DCR) module to expand the receptive field of the network and to avoid the loss of global features. Then, to extract more local information and reduce the computational complexity, we design the depthwise separable convolution residual (DSCR) module to learn the channel and position information in the image, which not only reduces parameters dramatically but also improves the local denoising performance. In addition, a parallel network is constructed by fusing the features extracted from each DCR module and DSCR module to improve the efficiency and reduce the complexity for training a denoising model. Finally, a reconstruction (REC) module aims to construct the clean image through the obtained noise deviation and the given noisy image. Due to the lack of ground-truth images in the real MR dataset, the performance of the proposed model was tested qualitatively and quantitatively on one simulated T1-weighted MR image dataset and then expanded to four real datasets. The experimental results show that the proposed 3D-Parallel-RicianNet network achieves performance superior to that of several state-of-the-art methods in terms of the peak signal-to-noise ratio, structural similarity index, and entropy metric. In particular, our method demonstrates powerful abilities in both noise suppression and structure preservation.

Evolution of Human Brain Atlases in Terms of Content, Applications, Functionality, and Availability

Human brain atlases have been evolving tremendously, propelled recently by brain big projects, and driven by sophisticated imaging techniques, advanced brain mapping methods, vast data, analytical strategies, and powerful computing. We overview here this evolution in four categories: content, applications, functionality, and availability, in contrast to other works limited mostly to content. Four atlas generations are distinguished: early cortical maps, print stereotactic atlases, early digital atlases, and advanced brain atlas platforms, and 5 avenues in electronic atlases spanning the last two generations. Content-wise, new electronic atlases are categorized into eight groups considering their scope, parcellation, modality, plurality, scale, ethnicity, abnormality, and a mixture of them. Atlas content developments in these groups are heading in 23 various directions. Application-wise, we overview atlases in neuroeducation, research, and clinics, including stereotactic and functional neurosurgery, neuroradiology, neurology, and stroke. Functionality-wise, tools and functionalities are addressed for atlas creation, navigation, individualization, enabling operations, and application-specific. Availability is discussed in media and platforms, ranging from mobile solutions to leading-edge supercomputers, with three accessibility levels. The major application-wise shift has been from research to clinical practice, particularly in stereotactic and functional neurosurgery, although clinical applications are still lagging behind the atlas content progress. Atlas functionality also has been relatively neglected until recently, as the management of brain data explosion requires powerful tools. We suggest that the future human brain atlas-related research and development activities shall be founded on and benefit from a standard framework containing the core virtual brain model cum the brain atlas platform general architecture.

Cerebral Blood Flow in Patients with Severe Aortic Valve Stenosis Undergoing Transcatheter Aortic Valve Implantation

BACKGROUND

Transcatheter aortic valve implantation (TAVI) is a minimally invasive, life‐saving treatment for patients with severe aortic valve stenosis that improves quality of life. We examined cardiac output and cerebral blood flow in patients undergoing TAVI to test the hypothesis that improved cardiac output after TAVI is associated with an increase in cerebral blood flow.

DESIGN

Prospective cohort study.

SETTING

European high‐volume tertiary multidisciplinary cardiac care.

PARTICIPANTS

Thirty‐one patients (78.3 ± 4.6 years; 61% female) with severe symptomatic aortic valve stenosis.

MEASUREMENTS

Noninvasive prospective assessment of cardiac output (L/min) by inert gas rebreathing and cerebral blood flow of the total gray matter (mL/100 g per min) using arterial spin labeling magnetic resonance imaging in resting state less than 24 hours before TAVI and at 3‐month follow‐up. Cerebral blood flow change was defined as the difference relative to baseline.

RESULTS

On average, cardiac output in patients with severe aortic valve stenosis increased from 4.0 ± 1.1 to 5.4 ± 2.4 L/min after TAVI (P = .003). The increase in cerebral blood flow after TAVI strongly varied between patients (7% ± 24%; P = .41) and related to the increase in cardiac output, with an 8.2% (standard error = 2.3%; P = .003) increase in cerebral blood flow per every additional liter of cardiac output following the TAVI procedure.

CONCLUSION

Following TAVI, there was an association of increase in cardiac output with increase in cerebral blood flow. These findings encourage future larger studies to determine the influence of TAVI on cerebral blood flow and cognitive function.

Microglial activation in early Alzheimer trajectory is associated with higher gray matter volume

Objective

To investigate the influence of microglial activation in the early stages of Alzheimer's disease trajectory, we assessed the relationship between microglial activation and gray matter volume and hippocampal volume in patients with mild cognitive impairment (MCI).

Methods

In this study, 55 participants (37 with early stages of MCI and 18 controls) underwent [¹¹C]PBR28 PET, a marker of microglial activation; volumetric MRI to evaluate gray matter and hippocampal volumes as well as clinical and neuropsychometric evaluation. [¹¹C]PBR28 V_T (volume of distribution) was calculated using arterial input function and Logan graphical analysis. Gray matter volume and hippocampal volumes were calculated from MRI for each participant. Statistical parametric mapping software was used to perform voxel-wise correlations and biological parametric mapping analysis. Amyloid status was assessed using [¹⁸F]flutemetamol PET.

Results

Higher [¹¹C]PBR28 V_T in different cortical areas correlated with higher gray matter volume in both amyloid-positive and -negative MCI. In addition, higher hippocampal volume correlated with higher cortical [¹¹C]PBR28 Logan V_T.

Conclusions

In this in vivo study, we have demonstrated that microglial activation quantified using [¹¹C]PBR28 PET was associated with higher gray matter volume and higher hippocampal volume in patients with MCI. This might suggest that microglial activation may not always be associated with neuronal damage, and indeed it may have a beneficial effect in the early stages of the Alzheimer trajectory. While further longitudinal studies are necessary, these findings have significant implications on therapeutic strategies targeting microglial activation.

Dual-phase [18F]florbetapir in frontotemporal dementia

PURPOSE

The PET tracer [18F]florbetapir is a specific fibrillar amyloid-beta (Aβ) biomarker. During the late scan phase (> 40 min), it provides pathological information about Aβ status. Early scan phase (0-10 min) can provide FDG-'like' information. The current investigation tested the feasibility of using florbetapir as a dual-phase biomarker in behavioural variant frontotemporal dementia (bvFTD).

METHODS

Eight bvFTD patients underwent [18F]florbetapir and [18]FDG-PET scans. Additionally, ten healthy controls and ten AD patients underwent florbetapir-PET only. PET data were acquired dynamically for 60-min post-injection. The bvFTD PET data were used to define an optimal time window, representing blood flow-related pseudo-metabolism ('pseudo-FDG'), of florbetapir data that maximally correlated with the corresponding real FDG SUVR (40-60 min) in a composite neocortical FTD region.

RESULTS

A 2 to 5-min time window post-injection of the florbetapir-PET data provided the largest correlation (Pearson's r = 0.79, p = 0.02) to the FDG data. The pseudo-FDG images demonstrated strong internal consistency with actual FDG data and were also visually consistent with the bvFTD patients' hypometabolic profiles. The ability to identify bvFTD from blind visual rating of pseudo-FDG images was consistent with previous reports using FDG data (sensitivity = 75%, specificity = 85%).

CONCLUSIONS

This investigation demonstrates that early phase florbetapir uptake shows a reduction of frontal lobe perfusion in bvFTD, similar to metabolic findings with FDG. Thus, dynamic florbetapir scans can serve as a dual-phase biomarker in dementia patients to distinguish FTD from AD and cognitively normal elderly, removing the need for a separate FDG-PET scan in challenging dementia cases.

Imaging of Microglial Activation in Alzheimer's Disease by [11C]PBR28 PET

Deficits in neuronal function and synaptic plasticity in Alzheimer's disease (AD) are believed to be linked to microglial activation. A hallmark of reactive microglia is the upregulation of mitochondrial translocator protein (TSPO) expression. Positron emission tomography (PET) is a nuclear imaging technique that measures the distribution of trace doses of radiolabeled compounds in the body over time. PET imaging using the 2nd generation TSPO tracer [¹¹C]PBR28 provides an opportunity for accurate visualization and quantification of changes in microglial density in transgenic mouse models of Alzheimer's disease (AD). Here, we describe the methodology for the in vivo use of [¹¹C]PBR28 in AD patients and the 5XFAD transgenic mouse model of AD and compare the results against healthy individuals and wild-type controls. To confirm the results, autoradiography with [³H]PBR28 and immunochemistry was carried out in the same mouse brains. Our data shows that [¹¹C]PBR28 is suitable as a tool for in vivo monitoring of microglial activation and may be useful to assess treatment response in future studies.

Biased and unbiased perceptual decision-making on vocal emotions

Perceptual decision-making on emotions involves gathering sensory information about the affective state of another person and forming a decision on the likelihood of a particular state. These perceptual decisions can be of varying complexity as determined by different contexts. We used functional magnetic resonance imaging and a region of interest approach to investigate the brain activation and functional connectivity behind two forms of perceptual decision-making. More complex unbiased decisions on affective voices recruited an extended bilateral network consisting of the posterior inferior frontal cortex, the orbitofrontal cortex, the amygdala, and voice-sensitive areas in the auditory cortex. Less complex biased decisions on affective voices distinctly recruited the right mid inferior frontal cortex, pointing to a functional distinction in this region following decisional requirements. Furthermore, task-induced neural connectivity revealed stronger connections between these frontal, auditory, and limbic regions during unbiased relative to biased decision-making on affective voices. Together, the data shows that different types of perceptual decision-making on auditory emotions have distinct patterns of activations and functional coupling that follow the decisional strategies and cognitive mechanisms involved during these perceptual decisions.

Assessing Inflammation in Acute Intracerebral Hemorrhage with PK11195 PET and Dynamic Contrast-Enhanced MRI

BACKGROUND AND PURPOSE

Studies in animal models suggest that inflammation is a major contributor to secondary injury after intracerebral hemorrhage (ICH). Direct, noninvasive monitoring of inflammation in the human brain after ICH will facilitate early-phase development of anti-inflammatory treatments. We sought to investigate the feasibility of multimodality brain imaging in subacute ICH.

METHODS

Acute ICH patients were recruited to undergo multiparametric MRI (including dynamic contrast-enhanced measurement of blood-brain barrier transfer constant (K^trans ) and PET with [¹¹ C]-(R)-PK11195). [¹¹ C]-(R)-PK11195 binds to the translocator protein 18 kDa (TSPO), which is rapidly upregulated in activated microglia. Circulating inflammatory markers were measured at the time of PET.

RESULTS

Five patients were recruited to this feasibility study with imaging between 5 and 16 days after onset. Etiologies included hypertension-related small vessel disease, cerebral amyloid angiopathy (CAA), cavernoma, and arteriovenous malformation (AVM). [¹¹ C]-(R)-PK11195 binding was low in all hematomas and 2 (patient 2 [probable CAA] and 4 [AVM]) cases showed widespread increase in binding in the perihematomal region versus contralateral. All had increased K^trans in the perihematomal region (mean difference = 2.2 × 10^-3 minute^-1 ; SD = 1.6 × 10^-3 minute^-1 ) versus contralateral. Two cases (patients 1 [cavernoma] and 4 [AVM]) had delayed surgery (3 and 12 months post-onset, respectively) with biopsies showing intense microglial activation in perilesional tissue.

CONCLUSIONS

Our study demonstrates for the first time the feasibility of performing complex multimodality brain imaging for noninvasive monitoring of neuroinflammation for this severe stroke subtype.

Gyri of the human parietal lobe: Volumes, spatial extents, automatic labelling, and probabilistic atlases

Accurately describing the anatomy of individual brains enables interlaboratory communication of functional and developmental studies and is crucial for possible surgical interventions. The human parietal lobe participates in multimodal sensory integration including language processing and also contains the primary somatosensory area. We describe detailed protocols to subdivide the parietal lobe, analyze morphological and volumetric characteristics, and create probabilistic atlases in MNI152 stereotaxic space. The parietal lobe was manually delineated on 3D T1 MR images of 30 healthy subjects and divided into four regions: supramarginal gyrus (SMG), angular gyrus (AG), superior parietal lobe (supPL) and postcentral gyrus (postCG). There was the expected correlation of male gender with larger brain and intracranial volume. We examined a wide range of anatomical features of the gyri and the sulci separating them. At least a rudimentary primary intermediate sulcus of Jensen (PISJ) separating SMG and AG was identified in nearly all (59/60) hemispheres. Presence of additional gyri in SMG and AG was related to sulcal features and volumetric characteristics. The parietal lobe was slightly (2%) larger on the left, driven by leftward asymmetries of the postCG and SMG. Intersubject variability was highest for SMG and AG, and lowest for postCG. Overall the morphological characteristics tended to be symmetrical, and volumes also tended to covary between hemispheres. This may reflect developmental as well as maturation factors. To assess the accuracy with which the labels can be used to segment newly acquired (unlabelled) T1-weighted brain images, we applied multi-atlas label propagation software (MAPER) in a leave-one-out experiment and compared the resulting automatic labels with the manually prepared ones. The results showed strong agreement (mean Jaccard index 0.69, corresponding to a mean Dice index of 0.82, average mean volume error of 0.6%). Stereotaxic probabilistic atlases of each subregion were obtained. They illustrate the physiological brain torque, with structures in the right hemisphere positioned more anteriorly than in the left, and right/left positional differences of up to 10 mm. They also allow an assessment of sulcal variability, e.g. low variability for parietooccipital fissure and cingulate sulcus. Illustrated protocols, individual label sets, probabilistic atlases, and a maximum-probability atlas which takes into account surrounding structures are available for free download under academic licences.

Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion

Clinical annotations, such as voxel-wise binary or probabilistic tissue segmentations, structural parcellations, pathological regions-of-interest and anatomical landmarks are key to many clinical studies. However, due to the time consuming nature of manually generating these annotations, they tend to be scarce and limited to small subsets of data. This work explores a novel framework to propagate voxel-wise annotations between morphologically dissimilar images by diffusing and mapping the available examples through intermediate steps. A spatially-variant graph structure connecting morphologically similar subjects is introduced over a database of images, enabling the gradual diffusion of information to all the subjects, even in the presence of large-scale morphological variability. We illustrate the utility of the proposed framework on two example applications: brain parcellation using categorical labels and tissue segmentation using probabilistic features. The application of the proposed method to categorical label fusion showed highly statistically significant improvements when compared to state-of-the-art methodologies. Significant improvements were also observed when applying the proposed framework to probabilistic tissue segmentation of both synthetic and real data, mainly in the presence of large morphological variability.

Amyloid PET in European and North American cohorts; and exploring age as a limit to clinical use of amyloid imaging

Purpose

Several radiotracers that bind to fibrillar amyloid-beta in the brain have been developed and used in various patient cohorts. This study aimed to investigate the comparability of two amyloid positron emission tomography (PET) tracers as well as examine how age affects the discriminative properties of amyloid PET imaging.

Methods

Fifty-one healthy controls (HCs), 72 patients with mild cognitive impairment (MCI) and 90 patients with Alzheimer’s disease (AD) from a European cohort were scanned with [11C]Pittsburgh compound-B (PIB) and compared with an age-, sex- and disease severity-matched population of 51 HC, 72 MCI and 84 AD patients from a North American cohort who were scanned with [18F]Florbetapir. An additional North American population of 246 HC, 342 MCI and 138 AD patients with a Florbetapir scan was split by age (55–75 vs 76–93 y) into groups matched for gender and disease severity. PET template-based analyses were used to quantify regional tracer uptake.

Results

The mean regional uptake patterns were similar and strong correlations were found between the two tracers across the regions of interest in HC (ρ = 0.671, p = 0.02), amyloid-positive MCI (ρ = 0.902, p < 0.001) and AD patients (ρ = 0.853, p < 0.001). The application of the Florbetapir cut-off point resulted in a higher proportion of amyloid-positive HC and a lower proportion of amyloid-positive AD patients in the older group (28 and 30 %, respectively) than in the younger group (19 and 20 %, respectively).

Conclusions

These results illustrate the comparability of Florbetapir and PIB in unrelated but matched patient populations. The role of amyloid PET imaging becomes increasingly important with increasing age in the diagnostic assessment of clinically impaired patients.

Electronic supplementary material

The online version of this article (doi:10.1007/s00259-015-3115-5) contains supplementary material, which is available to authorized users.

Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition

Multi-atlas patch-based label fusion methods have been successfully used to improve segmentation accuracy in many important medical image analysis applications. In general, to achieve label fusion a single target image is first registered to several atlas images. After registration a label is assigned to each target point in the target image by determining the similarity between the underlying target image patch (centered at the target point) and the aligned image patch in each atlas image. To achieve the highest level of accuracy during the label fusion process it's critical for the chosen patch similarity measurement to accurately capture the tissue/shape appearance of the anatomical structure. One major limitation of existing state-of-the-art label fusion methods is that they often apply a fixed size image patch throughout the entire label fusion procedure. Doing so may severely affect the fidelity of the patch similarity measurement, which in turn may not adequately capture complex tissue appearance patterns expressed by the anatomical structure. To address this limitation, we advance state-of-the-art by adding three new label fusion contributions: First, each image patch is now characterized by a multi-scale feature representation that encodes both local and semi-local image information. Doing so will increase the accuracy of the patch-based similarity measurement. Second, to limit the possibility of the patch-based similarity measurement being wrongly guided by the presence of multiple anatomical structures in the same image patch, each atlas image patch is further partitioned into a set of label-specific partial image patches according to the existing labels. Since image information has now been semantically divided into different patterns, these new label-specific atlas patches make the label fusion process more specific and flexible. Lastly, in order to correct target points that are mislabeled during label fusion, a hierarchical approach is used to improve the label fusion results. In particular, a coarse-to-fine iterative label fusion approach is used that gradually reduces the patch size. To evaluate the accuracy of our label fusion approach, the proposed method was used to segment the hippocampus in the ADNI dataset and 7.0 T MR images, sub-cortical regions in LONI LBPA40 dataset, mid-brain regions in SATA dataset from MICCAI 2013 segmentation challenge, and a set of key internal gray matter structures in IXI dataset. In all experiments, the segmentation results of the proposed hierarchical label fusion method with multi-scale feature representations and label-specific atlas patches are more accurate than several well-known state-of-the-art label fusion methods.

Learning to rank atlases for multiple-atlas segmentation

Recently, multiple-atlas segmentation (MAS) has achieved a great success in the medical imaging area. The key assumption is that multiple atlases have greater chances of correctly labeling a target image than a single atlas. However, the problem of atlas selection still remains unexplored. Traditionally, image similarity is used to select a set of atlases. Unfortunately, this heuristic criterion is not necessarily related to the final segmentation performance. To solve this seemingly simple but critical problem, we propose a learning-based atlas selection method to pick up the best atlases that would lead to a more accurate segmentation. Our main idea is to learn the relationship between the pairwise appearance of observed instances (i.e., a pair of atlas and target images) and their final labeling performance (e.g., using the Dice ratio). In this way, we select the best atlases based on their expected labeling accuracy. Our atlas selection method is general enough to be integrated with any existing MAS method. We show the advantages of our atlas selection method in an extensive experimental evaluation in the ADNI, SATA, IXI, and LONI LPBA40 datasets. As shown in the experiments, our method can boost the performance of three widely used MAS methods, outperforming other learning-based and image-similarity-based atlas selection methods.

The contribution of cortical thickness and surface area to gray matter asymmetries in the healthy human brain

Human cortical gray matter (GM) is structurally asymmetrical and this asymmetry has been discussed to be partly responsible for functional lateralization of human cognition and behavior. Past studies on brain asymmetry have shown mixed results so far, with some studies focusing on the global shapes of the brain's surface, such as gyrification patterns, while others focused on regional brain volumes. In this study, we investigated cortical GM asymmetries in a large sample of right-handed healthy volunteers (n = 101), using a surface-based method which allows to analyze brain cortical thickness and surface area separately. As a result, substantially different patterns of symmetry emerged between cortical thickness and surface area measures. In general, asymmetry is more prominent in the measure of surface compared to that of thickness. Such a detailed investigation of structural asymmetries in the normal brain contributes largely to our knowledge of normal brain development and also offers insights into the neurodevelopmental basis of psychiatric disorders, such as schizophrenia.

Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures

Healthy ageing coincides with a progressive decline of brain gray matter (GM) ultimately affecting the entire brain. For a long time, manual delineation-based volumetry within predefined regions of interest (ROI) has been the gold standard for assessing such degeneration. Voxel-Based Morphometry (VBM) offers an automated alternative approach that, however, relies critically on the segmentation and spatial normalization of a large collection of images from different subjects. This can be achieved via different algorithms, with SPM5/SPM8, DARTEL of SPM8 and FSL tools (FAST, FNIRT) being three of the most frequently used. We complemented these voxel based measurements with a ROI based approach, whereby the ROIs are defined by transforms of an atlas (containing different tissue probability maps as well as predefined anatomic labels) to the individual subject images in order to obtain volumetric information at the level of the whole brain or within separate ROIs. Comparing GM decline between 21 young subjects (mean age 23) and 18 elderly (mean age 66) revealed that volumetric measurements differed significantly between methods. The unified segmentation/normalization of SPM5/SPM8 revealed the largest age-related differences and DARTEL the smallest, with FSL being more similar to the DARTEL approach. Method specific differences were substantial after segmentation and most pronounced for the cortical structures in close vicinity to major sulci and fissures. Our findings suggest that algorithms that provide only limited degrees of freedom for local deformations (such as the unified segmentation and normalization of SPM5/SPM8) tend to overestimate between-group differences in VBM results when compared to methods providing more flexible warping. This difference seems to be most pronounced if the anatomy of one of the groups deviates from custom templates, a finding that is of particular importance when results are compared across studies using different VBM methods.

The relationship between reward and punishment processing and the 5-HT1A receptor as shown by PET

RATIONALE

The serotonin (5-HT) system has been reported to be involved in decision-making. A key component of this neurotransmitter system is the 5-HT1A receptor, and research is beginning to show how this receptor can influence decision-making. However, this relationship has rarely been studied in humans.

OBJECTIVES

This study assessed whether individual variability in 5-HT1A availability correlates with decision-making in healthy volunteers.

METHODS

We measured regional availability of the 5-HT1A receptor in the hippocampal complex and striatum using positron emission tomography and correlated this with performance on two decision-making tasks measuring sensitivity to probability, rewards and punishments and temporal discounting, respectively.

RESULTS

No relationship between decision-making behaviour and 5-HT1A availability in the striatum was found. However, a positive correlation was detected between participants' 5-HT1A availability in the hippocampal complex and their sensitivity to the probability of winning. Furthermore, there was a negative correlation between the degree to which participants discounted future rewards and 5-HT1A availability in the hippocampal complex.

CONCLUSIONS

These data support a role for the 5-HT1A receptor in the aberrant decision-making that can occur in neuropsychiatric disorders such as depression.

[¹¹C]-(R)PK11195 tracer kinetics in the brain of glioma patients and a comparison of two referencing approaches

PURPOSE

Translocator protein (TSPO) is a biomarker of neuroinflammation that can be imaged by PET using [¹¹C]-(R)PK11195. We sought to characterize the [¹¹C]-(R)PK11195 kinetics in gliomas of different histotypes and grades, and to compare two reference tissue input functions (supervised cluster analysis versus cerebellar grey matter) for the estimation of [¹¹C]-(R)PK11195 binding in gliomas and surrounding brain structures.

METHODS

Twenty-three glioma patients and ten age-matched controls underwent structural MRI and dynamic [¹¹C]-(R)PK11195 PET scans. Tissue time-activity curves (TACs) were extracted from tumour regions as well as grey matter (GM) and white matter (WM) of the brains. Parametric maps of binding potential (BPND) were generated with the simplified reference tissue model using the two input functions, and were compared with each other. TSPO expression was assessed in tumour tissue sections by immunohistochemistry.

RESULTS

Three types of regional kinetics were observed in individual tumour TACs: GM-like kinetics (n=6, clearance of the tracer similar to that in cerebellar GM), WM-like kinetics (n=8, clearance of the tracer similar to that in cerebral WM) and a form of mixed kinetics (n=9, intermediate rate of clearance). Such kinetic patterns differed between low-grade astrocytomas (WM-like kinetics) and oligodendrogliomas (GM-like and mixed kinetics), but were independent of tumour grade. There was good agreement between parametric maps of BPND derived from the two input functions in all controls and 10 of 23 glioma patients. In 13 of the 23 patients, BPND values derived from the supervised cluster input were systematically smaller than those using the cerebellar input. Immunohistochemistry confirmed that TSPO expression increased with tumour grade.

CONCLUSION

The three types of [¹¹C]-(R)PK11195 kinetics in gliomas are determined in part by tracer delivery, and indicated that kinetic analysis is a valuable tool in the study of gliomas with the potential for in vivo discrimination between low-grade astrocytomas and oligodendrogliomas. Supervised cluster and cerebellar input functions produced consistent BPND estimates in approximately half of the gliomas investigated, but had a systematic difference in the remainder. The cerebellar input is preferred based on theoretical and practical considerations.

Relationships between non-pathological dream-enactment and mirror behaviors

Dream-enacting behaviors (DEBs) are behavioral expressions of forceful dream images often occurring during sleep-to-wakefulness transitions. We propose that DEBs reflect brain activity underlying social cognition, in particular, motor-affective resonance generated by the mirror neuron system. We developed a Mirror Behavior Questionnaire (MBQ) to assess some dimensions of mirror behaviors and investigated relationships between MBQ scores and DEBs in a large of university undergraduate cohort. MBQ scores were normally distributed and described by a four-factor structure (Empathy/Emotional Contagion, Behavioral Imitation, Sleepiness/Anger Contagion, Motor Skill Imitation). DEB scores correlated positively with MBQ total and factor scores even with social desirability, somnambulism and somniloquy controlled. Emotion-specific DEB items correlated with corresponding emotion-specific MBQ items, especially crying and smiling. Results provide preliminary evidence for cross-state relationships between propensities for dream-enacting and mirror behaviors--especially behaviors involving motor-affective resonance--and our suggestion that motor-affective resonance mediates dream-enactment imagery during sleep and emotional empathy during waking.

Magnetic resonance imaging of the newborn brain: automatic segmentation of brain images into 50 anatomical regions

We studied methods for the automatic segmentation of neonatal and developing brain images into 50 anatomical regions, utilizing a new set of manually segmented magnetic resonance (MR) images from 5 term-born and 15 preterm infants imaged at term corrected age called ALBERTs. Two methods were compared: individual registrations with label propagation and fusion; and template based registration with propagation of a maximum probability neonatal ALBERT (MPNA). In both cases we evaluated the performance of different neonatal atlases and MPNA, and the approaches were compared with the manual segmentations by means of the Dice overlap coefficient. Dice values, averaged across regions, were 0.81±0.02 using label propagation and fusion for the preterm population, and 0.81±0.02 using the single registration of a MPNA for the term population. Segmentations of 36 further unsegmented target images of developing brains yielded visibly high-quality results. This registration approach allows the rapid construction of automatically labeled age-specific brain atlases for neonates and the developing brain.

STEPS: Similarity and Truth Estimation for Propagated Segmentations and its application to hippocampal segmentation and brain parcelation

Anatomical segmentation of structures of interest is critical to quantitative analysis in medical imaging. Several automated multi-atlas based segmentation propagation methods that utilise manual delineations from multiple templates appear promising. However, high levels of accuracy and reliability are needed for use in diagnosis or in clinical trials. We propose a new local ranking strategy for template selection based on the locally normalised cross correlation (LNCC) and an extension to the classical STAPLE algorithm by Warfield et al. (2004), which we refer to as STEPS for Similarity and Truth Estimation for Propagated Segmentations. It addresses the well-known problems of local vs. global image matching and the bias introduced in the performance estimation due to structure size. We assessed the method on hippocampal segmentation using a leave-one-out cross validation with optimised model parameters; STEPS achieved a mean Dice score of 0.925 when compared with manual segmentation. This was significantly better in terms of segmentation accuracy when compared to other state-of-the-art fusion techniques. Furthermore, due to the finer anatomical scale, STEPS also obtains more accurate segmentations even when using only a third of the templates, reducing the dependence on large template databases. Using a subset of Alzheimer's Disease Neuroimaging Initiative (ADNI) scans from different MRI imaging systems and protocols, STEPS yielded similarly accurate segmentations (Dice=0.903). A cross-sectional and longitudinal hippocampal volumetric study was performed on the ADNI database. Mean±SD hippocampal volume (mm(3)) was 5195 ± 656 for controls; 4786 ± 781 for MCI; and 4427 ± 903 for Alzheimer's disease patients and hippocampal atrophy rates (%/year) of 1.09 ± 3.0, 2.74 ± 3.5 and 4.04 ± 3.6 respectively. Statistically significant (p<10(-3)) differences were found between disease groups for both hippocampal volume and volume change rates. Finally, STEPS was also applied in a multi-label segmentation propagation scenario using a leave-one-out cross validation, in order to parcellate 83 separate structures of the brain. Comparisons of STEPS with state-of-the-art multi-label fusion algorithms showed statistically significant segmentation accuracy improvements (p<10(-4)) in several key structures.

Language-selective and domain-general regions lie side by side within Broca's area

In 1861, Paul Broca stood up before the Anthropological Society of Paris and announced that the left frontal lobe was the seat of speech. Ever since, Broca’s eponymous brain region has served as a primary battleground for one of the central debates in the science of the mind and brain: Is human cognition produced by highly specialized brain regions, each conducting a specific mental process, or instead by more general-purpose brain mechanisms, each broadly engaged in a wide range of cognitive tasks? For Broca’s area, the debate focuses on specialization for language versus domain-general functions such as hierarchical structure building (e.g., [1, 2]), aspects of action processing (e.g., [3]), working memory (e.g., [4]), or cognitive control (e.g., [5–7]). Here, using single-subject fMRI, we find that both ideas are right: Broca’s area contains two sets of subregions lying side by side, one quite specifically engaged in language processing, surrounded by another that is broadly engaged across a wide variety of tasks and content domains.

A European multicentre PET study of fibrillar amyloid in Alzheimer's disease

Purpose

Amyloid PET tracers have been developed for in vivo detection of brain fibrillar amyloid deposition in Alzheimer’s disease (AD). To serve as an early biomarker in AD the amyloid PET tracers need to be analysed in multicentre clinical studies.

Methods

In this study 238 [¹¹C]Pittsburgh compound-B (PIB) datasets from five different European centres were pooled. Of these 238 datasets, 18 were excluded, leaving [¹¹C]PIB datasets from 97 patients with clinically diagnosed AD (mean age 69 ± 8 years), 72 patients with mild cognitive impairment (MCI; mean age 67.5 ± 8 years) and 51 healthy controls (mean age 67.4 ± 6 years) available for analysis. Of the MCI patients, 64 were longitudinally followed for 28 ± 15 months. Most participants (175 out of 220) were also tested for apolipoprotein E (ApoE) genotype.

Results

[¹¹C]PIB retention in the neocortical and subcortical brain regions was significantly higher in AD patients than in age-matched controls. Intermediate [¹¹C]PIB retention was observed in MCI patients, with a bimodal distribution (64 % MCI PIB-positive and 36 % MCI PIB-negative), which was significantly different the pattern in both the AD patients and controls. Higher [¹¹C]PIB retention was observed in MCI ApoE ε4 carriers compared to non-ApoE ε4 carriers (p < 0.005). Of the MCI PIB-positive patients, 67 % had converted to AD at follow-up while none of the MCI PIB-negative patients converted.

Conclusion

This study demonstrated the robustness of [¹¹C]PIB PET as a marker of neocortical fibrillar amyloid deposition in brain when assessed in a multicentre setting. MCI PIB-positive patients showed more severe memory impairment than MCI PIB-negative patients and progressed to AD at an estimated rate of 25 % per year. None of the MCI PIB-negative patients converted to AD, and thus PIB negativity had a 100 % negative predictive value for progression to AD. This supports the notion that PIB-positive scans in MCI patients are an indicator of prodromal AD.

Electronic supplementary material

The online version of this article (doi:10.1007/s00259-012-2237-2) contains supplementary material, which is available to authorized users.

Increased PK11195 PET binding in the cortex of patients with MS correlates with disability

OBJECTIVE

Activated microglia are thought to play a major role in cortical gray matter (GM) demyelination in multiple sclerosis (MS). Our objective was to evaluate microglial activation in cortical GM of patients with MS in vivo and to explore its relationship to measures of disability.

METHODS

Using PET and optimized modeling and segmentation procedures, we investigated cortical (11)C-PK11195 (PK11195) binding in patients with relapsing-remitting MS (RRMS), patients with secondary progressive MS (SPMS), and healthy controls. Disability was assessed with the Expanded Disability Status Scale (EDSS) and Multiple Sclerosis Impact Scale (MSIS-29).

RESULTS

Patients with MS showed increased cortical GM PK11195 binding relative to controls, which was multifocal and highest in the postcentral, middle frontal, anterior orbital, fusiform, and parahippocampal gyri. Patients with SPMS also showed additional increases in precentral, superior parietal, lingual and anterior superior, medial and inferior temporal gyri. Total cortical GM PK11195 binding correlated with EDSS scores, with a stronger correlation for the subgroup of patients with SPMS. In patients with SPMS, PK11195 binding also correlated with MSIS-29 scores. No correlation with disability measures was seen for PK11195 binding in white matter. Higher EDSS scores correlated with higher levels of GM PK11195 binding in the postcentral gyrus for patients with RRMS and in precentral gyrus for those with SPMS.

CONCLUSIONS

Microglial activation in cortical GM of patients with MS can be assessed in vivo. The distribution is not uniform and shows a relationship to clinical disability. We speculate that the increased PK11195 binding corresponds to enhanced microglial activation described in postmortem SPMS cortical GM.

Sulcal morphology and volume of Broca's area linked to handedness and sex

We investigated the effect of handedness and sex on: (i) sulcal contours defining PO and PTR and (ii) volume estimates of PO and PTR subfields in 40 left- and 42 right-handers. Results show an effect of handedness on discontinuity of the inferior frontal sulcus (IFS: P<0.01). Discontinuity of IFS was observed in: 43% left- and 62% right hemispheres in right-handers and in 65% left- and 48% right-hemispheres in left-handers. PO volume asymmetry was rightward in left-handed males (P=0.007) and females (P=0.02), showed a leftward trend in right-handed males (P=0.06), and was non-asymmetrical in right-handed females (P=0.96, i.e. left- and right-hemisphere PO volumes did not differ significantly). PO volume asymmetry in males differed significantly between handedness groups (P=0.001). Findings indicate a high degree of variability in the sulcal contours of PO and PTR and volume asymmetry of PO: the factors sex and handedness can explain some of this variability.

Characterisation of the contribution of the GABA-benzodiazepine α1 receptor subtype to [(11)C]Ro15-4513 PET images

This positron emission tomography (PET) study aimed to further define selectivity of [(11)C]Ro15-4513 binding to the GABARα5 relative to the GABARα1 benzodiazepine receptor subtype. The impact of zolpidem, a GABARα1-selective agonist, on [(11)C]Ro15-4513, which shows selectivity for GABARα5, and the nonselective benzodiazepine ligand [(11)C]flumazenil binding was assessed in humans. Compartmental modelling of the kinetics of [(11)C]Ro15-4513 time-activity curves was used to describe distribution volume (V(T)) differences in regions populated by different GABA receptor subtypes. Those with low α5 were best fitted by one-tissue compartment models; and those with high α5 required a more complex model. The heterogeneity between brain regions suggested spectral analysis as a more appropriate method to quantify binding as it does not a priori specify compartments. Spectral analysis revealed that zolpidem caused a significant V(T) decrease (~10%) in [(11)C]flumazenil, but no decrease in [(11)C]Ro15-4513 binding. Further analysis of [(11)C]Ro15-4513 kinetics revealed additional frequency components present in regions containing both α1 and α5 subtypes compared with those containing only α1. Zolpidem reduced one component (mean±s.d.: 71%±41%), presumed to reflect α1-subtype binding, but not another (13%±22%), presumed to reflect α5. The proposed method for [(11)C]Ro15-4513 analysis may allow more accurate selective binding assays and estimation of drug occupancy for other nonselective ligands.

Geodesic information flows

Homogenising the availability of manually generated information in large databases has been a key challenge of medical imaging for many years. Due to the time consuming nature of manually segmenting, parcellating and localising landmarks in medical images, these sources of information tend to be scarce and limited to small, and sometimes morphologically similar, subsets of data. In this work we explore a new framework where these sources of information can be propagated to morphologically dissimilar images by diffusing and mapping the information through intermediate steps. The spatially variant data embedding uses the local morphology and intensity similarity between images to diffuse the information only between locally similar images. This framework can thus be used to propagate any information from any group of subject to every other subject in a database with great accuracy. Comparison to state-of-the-art propagation methods showed highly statistically significant (p < 10(-4)) improvements in accuracy when propagating both structural parcelations and brain segmentations geodesically.

Cortical folding analysis on patients with Alzheimer's disease and mild cognitive impairment

Cortical thinning is a widely used and powerful biomarker for measuring disease progression in Alzheimer's disease (AD). However, there has been little work on the effect of atrophy on the cortical folding patterns. In this study, we examined whether the cortical folding could be used as a biomarker of AD. Cortical folding metrics were computed on 678 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. For each subject, the boundary between grey matter and white matter was extracted using a level set technique. At each point on the boundary two metrics characterising folding, curvedness and shape index, were generated. Joint histograms using these metrics were calculated for five regions of interest (ROIs): frontal, temporal, occipital, and parietal lobes as well as the cingulum. Pixelwise statistical maps were generated from the joint histograms using permutations tests. In each ROI, a significant reduction was observed between controls and AD in areas associated with the sulcal folds, suggesting a sulcal opening associated with neurodegeneration. When comparing to MCI patients, the regions of significance were smaller but overlapping with those regions found comparing controls to AD. It indicates that the differences in cortical folding are progressive and can be detected before formal diagnosis of AD. Our preliminary analysis showed a viable signal in the cortical folding patterns for Alzheimer's disease that should be explored further.

Transcranial direct current stimulation (tDCS) in a realistic head model

Distributions of current produced by transcranial direct current stimulation (tDCS) in humans were predicted by a finite-element model representing several individual and collective refinements over prior efforts. A model of the entire human head and brain was made using a finely meshed (1.1x1.1x1.4mm(3) voxel) tissue dataset derived from the MRI data set of a normal human brain. The conductivities of ten tissues were simulated (bone, scalp, blood, CSF, muscle, white matter, gray matter, sclera, fat, and cartilage). We then modeled the effect of placing a "stimulating" electrode with a saline-like conductivity over F3, and a similar "reference" electrode over a right supraorbital (RS) location, as well as the complements of these locations, to compare expectations derived from the simulation with experimental data also using these locations in terms of the presence or absence of subjective and objective effects. The sensitivity of the results to changes in conductivity values were examined by varying white matter conductivity over a factor of ten. Our simulations established that high current densities were found directly under the stimulating and reference electrodes, but values of the same order of magnitude occurred in other structures, and many areas of the brain that might be behaviorally active were also subjected to what may be substantial amounts of current. The modeling also suggests that more targeted stimulations might be achieved by different electrode topologies.

A robust method to estimate the intracranial volume across MRI field strengths (1.5T and 3T)

As population-based studies may obtain images from scanners with different field strengths, a method to normalize regional brain volumes according to intracranial volume (ICV) independent of field strength is needed. We found systematic differences in ICV estimation, tested in a cohort of healthy subjects (n = 5) that had been imaged using 1.5T and 3T scanners, and confirmed in two independent cohorts. This was related to systematic differences in the intensity of cerebrospinal fluid (CSF), with higher intensities for CSF located in the ventricles compared with CSF in the cisterns, at 3T versus 1.5T, which could not be removed with three different applied bias correction algorithms. We developed a method based on tissue probability maps in MNI (Montreal Neurological Institute) space and reverse normalization (reverse brain mask, RBM) and validated it against manual ICV measurements. We also compared it with alternative automated ICV estimation methods based on Statistical Parametric Mapping (SPM5) and Brain Extraction Tool (FSL). The proposed RBM method was equivalent to manual ICV normalization with a high intraclass correlation coefficient (ICC = 0.99) and reliable across different field strengths. RBM achieved the best combination of precision and reliability in a group of healthy subjects, a group of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) and can be used as a common normalization framework.

Strategies for the generation of parametric images of [11C]PIB with plasma input functions considering discriminations and reproducibility

Pittsburgh compound B or [11C]PIB is an amyloid imaging agent which shows a clear differentiation between subjects with Alzheimer's disease (AD) and controls. However the observed signal difference in other forms of dementia such as dementia with Lewy bodies (DLB) is smaller, and mild cognitively impaired (MCI) subjects and some healthy elderly normals may show intermediate levels of [11C]PIB binding. The cerebellum, a commonly used reference region for non-specific tracer uptake in [11C]PIB studies in AD may not be valid in Prion disorders or monogenic forms of AD. The aim of this work was to: 1-compare methods for generating parametric maps of [11C]PIB retention in tissue using a plasma input function in respect of their ability to discriminate between AD subjects and controls and 2-estimate the test-retest reproducibility in AD subjects. 12 AD subjects (5 of which underwent a repeat scan within 6 weeks) and 10 control subjects had 90 minute [11C]PIB dynamic PET scans, and arterial plasma input functions were measured. Parametric maps were generated with graphical analysis of reversible binding (Logan plot), irreversible binding (Patlak plot), and spectral analysis. Between group differentiation was calculated using Student's t-test and comparisons between different methods were made using p values. Reproducibility was assessed by intraclass correlation coefficients (ICC). We found that the 75 min value of the impulse response function showed the best group differentiation and had a higher ICC than volume of distribution maps generated from Logan and spectral analysis. Patlak analysis of [11C]PIB binding was the least reproducible.

Broca's area: nomenclature, anatomy, typology and asymmetry

In this review, we (i) describe the nomenclature of Broca's area and show how the circumscribed definition of Broca's area is disassociated from Broca's aphasia, (ii) describe in detail how the gross anatomy of Broca's area varies between people, and how the definitions vary between studies, (iii) attempt to reconcile the findings of structural asymmetry of Broca's area with the differences in methodological approaches, (iv) consider the functional significance of cytoarchitectonic definitions of Broca's area, and (v) critically elucidate the significance of circumscribed regions of cortex for language lateralisation and language development. Contrary to what has previously been reported in the literature, asymmetry of Broca's area has not been reproducibly demonstrated, particularly on a gross morphological level. This may be due to major inconsistencies in methodology (including different anatomical boundaries, measurement techniques and samples studied) or that the sulcal contours defining Broca's area are so naturally variable between people making a standard definition difficult. Cytoarchitectonic analyses more often than not report leftward asymmetry of some component of area 44 and/or area 45. If a structural asymmetry of Broca's area does exist, it is variable, which differs from that of the functional asymmetry of language, which is more consistent. One reason for this might be that the link between cellular architecture, connectivity and language function still remains to be elucidated. There is currently no convincing explanation to associate asymmetry of Broca's area with the lateralisation of language.

Towards construction of an ideal stereotactic brain atlas

BACKGROUND

The role of the brain atlas is changing in many aspects with the advancements in stereotactic and functional neurosurgery. Therefore, there is a critical need to construct a new atlas. This paper addresses the definition and construction of an atlas, ideal (in our opinion) for stereotactic and functional neurosurgery. The essence of the new atlas is not only its population-based structural and functional content, but also its continuous "self-updatability" with the new clinical results obtained.

METHOD

The ideal atlas defined here contains four major components: brain models, knowledge database, tools, and clinical results. Towards its creation, a multi-atlas is proposed. The construction of the initial version of the multi-atlas is detailed with the probabilistic functional atlas (PFA), interpolated Talairach-Tournoux atlas, and enhanced Schaltenbrand-Wahren atlas. These atlases are put in a spatial register by matching their AC-PC distances and heights of the thalamus; the Schaltenbrand coronal and sagittal microseries are scaled laterally to match the target structure centroids with the locations of the best targets of the PFA.

FINDINGS

Construction of an initial version of the ideal stereotactic atlas is feasible at present from the available resources. To achieve that, our three atlases (PFA, Talairach and Schaltenbrand) are enhanced and combined together. A single lateral scaling factor per target structure is feasible to co-register the Schaltenbrand atlas with PFA in four situations (compensated against the third ventricle, non-compensated, bilateral, and non-bilateral). The STN has to be stretched by 18% more than the VIM on the Schaltenbrand coronal microseries, and the VIM has to be compressed by 13% less than the STN on the Schaltenbrand sagittal microseries.

CONCLUSION

The new multi-atlas can potentially be more useful than the currently employed atlases and will facilitate further development of the ideal atlas for stereotactic and functional neurosurgery.

Sulcal variability, stereological measurement and asymmetry of Broca's area on MR images

Leftward volume asymmetry of the pars opercularis and pars triangularis may exist in the human brain, frequently referred to as Broca's area, given the functional asymmetries observed in this region with regard to language expression. However, post-mortem and magnetic resonance imaging (MRI) studies have failed to consistently identify such a volumetric asymmetry. In the present study, an analysis of the asymmetry of sulco-gyral anatomy and volume of this anterior speech region was performed in combination with an analysis of the morphology and volume asymmetry of the planum temporale, located within the posterior speech region, in 50 healthy subjects using MRI. Variations in sulcal anatomy were documented according to strict classification schemes and volume estimation of the grey matter within the brain structures was performed using the Cavalieri method of stereology. Results indicated great variation in the morphology of and connectivity between the inferior frontal, inferior precentral and diagonal sulci. There were significant inter-hemispheric differences in the presence of (1) the diagonal sulcus within the pars opercularis, and (2) horizontal termination of the posterior Sylvian fissure (relative to upward oblique termination), both with an increased leftward incidence. Double parallel inferior precentral sulci and absent anterior rami of the Sylvian fissure prevented stereological measurements in five subjects. Therefore volumes were obtained from 45 subjects. There was a significant leftward volume asymmetry of the pars opercularis (P = 0.02), which was significantly related to the asymmetrical presence of the diagonal sulcus (P < 0.01). Group-wise pars opercularis volume asymmetry did not exist when a diagonal sulcus was present in both or neither hemispheres. There was no significant volume asymmetry of the pars triangularis. There was a significant leftward volume asymmetry of the planum temporale (P < 0.001), which was significantly associated with the shape of the posterior Sylvian fissure as a unilateral right or left upward oblique termination was always associated with leftward or rightward volume asymmetry respectively (P < 0.01). There was no relationship between volume asymmetries of the anterior and posterior speech regions. Our findings illustrate the extent of morphological variability of the anterior speech region and demonstrate the difficulties encountered when determining volumetric asymmetries of the inferior frontal gyrus, particularly when sulci are discontinuous, absent or bifid. When the intrasulcal grey matter of this region is exhaustively sampled according to strict anatomical landmarks, the volume of the pars opercularis is leftward asymmetrical. This manuscript illustrates the importance of simultaneous consideration of brain morphology and morphometry in studies of cerebral asymmetry.

Automatic detection and quantification of hippocampal atrophy on MRI in temporal lobe epilepsy: a proof-of-principle study

In temporal lobe epilepsy (TLE), hippocampal atrophy (HA) is a marker of poor prognosis regarding seizure remission, but predicts success of anterior temporal lobe resection. Manual quantification of HA on MRI is time-consuming and limited by investigator availability. Normal ranges of hippocampal volumes, both in absolute terms and relative to intracranial volume, and of hippocampal asymmetry were defined using an automatic label propagation and decision fusion technique based on thirty manually derived atlases of healthy controls. Manual test-retest reliability and overlaps of automatically and manually determined hippocampal volumes were quantified with similarity indices (SIs). Correct clinical identification of ipsilateral HA, and contralaterally normal hippocampal volumes, was determined in nine patients with histologically confirmed hippocampal sclerosis in terms of volumes and asymmetry indices (AIs) for standard statistical thresholds and with receiver operating characteristic (ROC) analysis. Manual test-retest reliability was very high, with SIs between 0.87 and 0.90. Manual and automatic hippocampus labels overlapped with a SI of 0.83 on the unaffected but with 0.76 on the atrophic side. Accuracy was higher for less atrophic hippocampi. The automatic method correctly identified 6/9 HAs in terms of absolute volume, 7/9 in terms of relative volume at a standard 2 SD threshold, and 9/9 for AIs. ROC-determined thresholds allowed clinically desirable correct identification of all HAs (100% sensitivity) with 85-100% specificity for volumes, and 100% specificity for AIs. The method has the potential to automatically detect unilateral HA, but further work is needed to determine its performance in detecting clinically important bilateral disease.