Morphometric in vivo evidence of thalamic atrophy correlated with cognitive and motor dysfunction in Huntington's disease: The IMAGE-HD study

In Huntington's disease (HD), neurodegeneration causes progressive atrophy to the striatum, cortical areas, and white matter tracts - components of corticostriatal circuitry. Such processes may affect the thalamus, a key circuit node. We investigated whether differences in dorsal thalamic morphology were detectable in HD, and whether thalamic atrophy was associated with neurocognitive, neuropsychiatric and motor dysfunction. Magnetic resonance imaging scans and clinical outcome measures were obtained from 34 presymptomatic HD (pre-HD), 29 early symptomatic HD (symp-HD), and 26 healthy control individuals who participated in the IMAGE-HD study. Manual region of interest (ROI) segmentation was conducted to measure dorsal thalamic volume, and thalamic ROI underwent shape analysis using the spherical harmonic point distribution method. The symp-HD group had significant thalamic volumetric reduction and global shape deflation, indicative of atrophy, compared to pre-HD and control groups. Thalamic atrophy significantly predicted neurocognitive and motor dysfunction within the symp-HD group only. Thalamic morphology differentiates symp-HD from pre-HD and healthy individuals. Thalamic changes may be one of the structural bases (endomorphotypes), of the endophenotypic neurocognitive and motor manifestations of disease. Future research should continue to investigate the thalamus as a potential in vivo biomarker of disease progression in HD.

Investigating the Added Value of FreeSurfer's Manual Editing Procedure for the Study of the Reading Network in a Pediatric Population

Insights into brain anatomy are important for the early detection of neurodevelopmental disorders, such as dyslexia. FreeSurfer is one of the most frequently applied automatized software tools to study brain morphology. However, quality control of the outcomes provided by FreeSurfer is often ignored and could lead to wrong statistical inferences. Additional manual editing of the data may be a solution, although not without a cost in time and resources. Past research in adults on comparing the automatized method of FreeSurfer with and without additional manual editing indicated that although editing may lead to significant differences in morphological measures between the methods in some regions, it does not substantially change the sensitivity to detect clinical differences. Given that automated approaches are more likely to fail in pediatric-and inherently more noisy-data, we investigated in the current study whether FreeSurfer can be applied fully automatically or additional manual edits of T1-images are needed in a pediatric sample. Specifically, cortical thickness and surface area measures with and without additional manual edits were compared in six regions of interest (ROIs) of the reading network in 5-to-6-year-old children with and without dyslexia. Results revealed that additional editing leads to statistical differences in the morphological measures, but that these differences are consistent across subjects and that the sensitivity to reveal statistical differences in the morphological measures between children with and without dyslexia is not affected, even though conclusions of marginally significant findings can differ depending on the method used. Thereby, our results indicate that additional manual editing of reading-related regions in FreeSurfer has limited gain for pediatric samples.

Thalamic and ventricular volumes predict motor response to deep brain stimulation for Parkinson's disease

BACKGROUND

Brain atrophy frequently occurs with Parkinson's disease (PD) and relates to increased motor symptoms of PD. The predictive value of neuroimaging-based measures of global and regional brain volume on motor outcomes in deep brain stimulation (DBS) remains unclear but potentially could improve patient selection and targeting.

OBJECTIVES

To determine the predictive value of preoperative volumetric MRI measures of cortical and subcortical brain volume on motor outcomes of subthalamic nucleus (STN) DBS in PD.

METHODS

Preoperative T1 3D MP-RAGE structural brain MRI images were analyzed for each participant to determine subcortical, ventricular, and cortical volume and thickness. Change in Unified Parkinson's Disease Rating Scale (UPDRS) scores for subsection 3, representing motor outcomes, was computed preoperatively and postoperatively following DBS programming in 86 participants. A multiple linear regression analysis was performed to investigate the relationship between volumetric data and the effect of DBS on UPDRS 3 scores.

RESULTS

Larger ventricular and smaller thalamic volumes predicted significantly less improvement of UPDRS 3 scores after STN DBS.

CONCLUSIONS

Our findings demonstrate in PD that regional brain volumes, in particular thalamic and ventricular volumes, predict motor outcomes after DBS. Differences in regional brain volumes may alter electrode targeting, reflect a specific disease trait such as postoperative progression of subclinical dementia, or directly interfere with the action of DBS.

Morphometric analysis of thalamic volume in progressive supranuclear palsy: In vivo evidence of regionally specific bilateral thalamic atrophy

We investigated whether differences were detectable in the volume and shape of the dorsal thalamus on magnetic resonance imaging in patients with progressive supranuclear palsy (PSP). Manual segmentation of the left and right thalami on magnetic resonance imaging scans occurred in 22 patients with clinically diagnosed PSP and 23 healthy controls; thalamic volumes (left, right, total) were calculated. Between group differences were explored by multivariate analysis of co-variance, using age and intracranial volume as covariates. Analysis of the shape of the thalamus was performed using the spherical harmonic point distribution method software package. Patients with PSP were found to have significant bilateral thalamic atrophy on magnetic resonance imaging; there was significant shape deflation over the anterior-lateral and anterior-ventral surfaces bilaterally, and over the right caudal thalamus. Recognizing decreased thalamic morphology in PSP patients in vivo may be an important component of an ensemble of diagnostic biomarkers in the future, particularly given the difficulty of distinguishing PSP from other Parkinsonian conditions early in the disease course.

Direct diffusion-based parcellation of the human thalamus

To assess stable anatomical features of the human thalamus, an unbiased diffusion tensor parcellation approach was used to segment thalamic substructures with similar spatial orientation. We determined localization, size and individual variations of 21 thalamic clusters in a group of 63 healthy human subjects (32 males/31 females). The laterality differences accounted for ± 6% and gender differences for ± 4% of the thalamic volume. Consecutively, five stable clusters in the anterior, medial, lateral and posterior thalamus were selected, which were common to 90% of all subjects and contained at least 10 voxels. These clusters could be assigned to the anteroventral nucleus (AN) group, the mediodorsal (MD) nucleus, the medial pulvinar (PuM), and the lateral nuclei group. The subcortical and cortical connectivity of these clusters revealed that: (1) the oblique cranio-caudal-oriented fibers of the AN cluster mainly connect to limbic structures, (2) the numerous dorso-frontal-oriented fibers of MD mainly project to the prefrontal cortex and the medial temporal lobe, (3) the fibers of the PuM running in parallel with the x-axis project to medio-occipital and medio-temporal areas and connect visual areas with the hippocampus and amygdala and via intrathalamic pathways with medio-frontal areas, and (4) the oblique caudo-cranial fibers of the two lateral clusters located anteriorly in the motor and posteriorly in the sensory thalamus are routing sensory-motor information from the brain stem via the internal capsule to pre- and peri-central regions of the cortex.

Human anterior thalamic nuclei are involved in emotion-attention interaction

Patients treated with deep brain stimulation (DBS) provide an opportunity to study affective processes in humans with "lesion on demand" at key nodes in the limbic circuitries, such as at the anterior thalamic nuclei (ANT). ANT has been suggested to play a role in emotional control with its connection to the orbitofrontal cortex and the anterior cingulate cortex. However, direct evidence for its role in emotional function in human subjects is lacking. Reported side effects of ANT-DBS in the treatment of refractory epilepsy include depression related symptoms. In line with these mood-related clinical side effects, we have previously reported that stimulating the anterior thalamus increased emotional interference in a visual attention task as indicated by prolonged reaction times due to threat-related emotional distractors. We used event-related potentials to investigate potential attentional mechanism behind this behavioural observation. We hypothesized that ANT-DBS leads to greater attention capture by threat-related distractors. We tested this hypothesis using centro-parietal N2-P3 peak-to-peak amplitude as a measure of allocated attentional resources. Six epileptic patients treated with deep brain stimulation at ANT participated in the study. Electroencephalography was recorded while the patients performed a computer based Executive-Reaction Time test with threat-related emotional distractors. During the task, either ANT or a thalamic control location was stimulated, or the stimulation was turned off. Stimulation of ANT was associated with increased centro-parietal N2-P3 amplitude and increased reaction time in the context of threat-related emotional distractors. We conclude that high frequency electric stimulation of ANT leads to greater attentional capture by emotional stimuli. This is the first study to provide direct evidence from human subjects with on-line electric manipulation of ANT for its role in emotion-attention interaction.

The thalamus as a putative biomarker in neurodegenerative disorders

OBJECTIVE

This review provides a brief account of the clinically relevant functional neuroanatomy of the thalamus, before considering the utility of various modalities utilized to image the thalamus and technical challenges therein, and going on to provide an overview of studies utilizing structural imaging techniques to map thalamic morphology in the spectrum of neurodegenerative disorders.

METHODS

A systematic search was conducted for peer-reviewed studies involving structural neuroimaging modalities investigating the morphology (shape and/or size) of the thalamus in the spectrum of neurodegenerative disorders.

RESULTS

While the precise role of the thalamus in the healthy brain remains unclear, there is a large body of knowledge accumulating which defines more precisely its functional connectivity within the connectome, and a burgeoning literature implicating its involvement in neurodegenerative disorders. It is proposed that correlation of clinical features with thalamic morphology (as a component of a quantifiable subcortical connectome) will provide a better understanding of neuropsychiatric dysfunction in various neurodegenerative disorders, potentially yielding clinically useful endophenotypes and disease biomarkers.

CONCLUSION

Thalamic biomarkers in the neurodegenerative disorders have great potential to provide clinically meaningful knowledge regarding not only disease onset and progression but may yield targets of and perhaps a way of gauging response to future disease-modifying modalities.

Validation of a protocol for manual segmentation of the thalamus on magnetic resonance imaging scans

We present a validated protocol for manual segmentation of the thalamus on T1-weighted magnetic resonance imaging (MRI) scans using brain image analysis software. The MRI scans of five normal control subjects were randomly selected from a larger cohort recruited from Lund University Hospital and Landskrona Hospital, Sweden. MRIs were performed using a 3.0T Philips MR scanner, with an eight-channel head coil, and high resolution images were acquired using a T1-weighted turbo field echo (T1 TFE) pulse sequence, with resulting voxel size 1×1×1 mm3. Manual segmentation of the left and right thalami and volume measurement was performed on 28-30 contiguous coronal slices, using ANALYZE 11.0 software. Reliability of image analysis was performed by measuring intra-class correlations between initial segmentation and random repeated segmentation of the left and right thalami (in total 10 thalami for segmentation); inter-rater reliability was measured using volumes obtained by two other experienced tracers. Intra-class correlations for two independent raters were 0.95 and 0.98; inter-class correlations between the expert rater and two independent raters were 0.92 and 0.98. We anticipate that mapping thalamic morphology in various neuropsychiatric disorders may yield clinically useful disease-specific biomarkers.

Damage to the dorsomedial thalamic nucleus, central lateral intralaminar thalamic nucleus, and midline thalamic nuclei on the right-side impair executive function and attention under conditions of high demand but not low demand

This study reports a patient, OG, with a unilateral right-sided thalamic lesion. High resolution 3T magnetic resonance imaging revealed damage to the parvicellular and magnocellular subdivisions of the dorsomedial thalamus (DMT), the central lateral intralaminar nucleus (also known as the paralamellar DMT), the paraventricular and the central medial midline thalamic nuclei. According to the neuropsychological literature, the DMT, the midline and intralaminar thalamic nuclei influence a wide array of cognitive functions by virtue of their modulatory influences on executive function and attention, and this is particularly indicated under conditions of low arousal or high cognitive demand. We explored this prediction in OG, and compared his performance on a range of low and high demand versions of tests that tapped executive function and attention to a group of 6 age- and IQ-matched controls. OG, without exception, significantly under performed on the high-demand attention and executive function tasks, but performed normally on the low-demand versions. These findings extend and refine current understanding of the effects of thalamic lesion on attention and executive function.

Local label learning (LLL) for subcortical structure segmentation: application to hippocampus segmentation

Automatic and reliable segmentation of subcortical structures is an important but difficult task in quantitative brain image analysis. Multi-atlas based segmentation methods have attracted great interest due to their promising performance. Under the multi-atlas based segmentation framework, using deformation fields generated for registering atlas images onto a target image to be segmented, labels of the atlases are first propagated to the target image space and then fused to get the target image segmentation based on a label fusion strategy. While many label fusion strategies have been developed, most of these methods adopt predefined weighting models that are not necessarily optimal. In this study, we propose a novel local label learning strategy to estimate the target image's segmentation label using statistical machine learning techniques. In particular, we use a L1-regularized support vector machine (SVM) with a k nearest neighbor (kNN) based training sample selection strategy to learn a classifier for each of the target image voxel from its neighboring voxels in the atlases based on both image intensity and texture features. Our method has produced segmentation results consistently better than state-of-the-art label fusion methods in validation experiments on hippocampal segmentation of over 100 MR images obtained from publicly available and in-house datasets. Volumetric analysis has also demonstrated the capability of our method in detecting hippocampal volume changes due to Alzheimer's disease.

Anterior limb of the internal capsule in schizotypal personality disorder: fiber-tract counting, volume, and anisotropy

Mounting evidence suggests that white matter abnormalities and altered subcortical-cortical connectivity may be central to the pathology of schizophrenia (SZ). The anterior limb of the internal capsule (ALIC) is an important thalamo-frontal white-matter tract shown to have volume reductions in SZ and to a lesser degree in schizotypal personality disorder (SPD). While fractional anisotropy (FA) and connectivity abnormalities in the ALIC have been reported in SZ, they have not been examined in SPD. In the current study, magnetic resonance (MRI) and diffusion tensor imaging (DTI) were obtained in age- and sex-matched individuals with SPD (n=33) and healthy controls (HCs; n=38). The ALIC was traced bilaterally on five equally spaced dorsal-to-ventral axial slices from each participant's MRI scan and co-registered to DTI for the calculation of FA. Tractography was used to examine tracts between the ALIC and two key Brodmann areas (BAs; BA10, BA45) within the dorsolateral prefrontal cortex (DLPFC). Compared with HCs, the SPD participants exhibited (a) smaller relative volume at the mid-ventral ALIC slice level but not the other levels; (b) normal FA within the ALIC; (c) fewer relative number of tracts between the most-dorsal ALIC levels and BA10 but not BA45 and (d) fewer dorsal ALIC-DLPFC tracts were associated with greater symptom severity in SPD. In contrast to prior SZ studies that report lower FA, individuals with SPD show sparing. Our findings are consistent with a pattern of milder thalamo-frontal dysconnectivity in SPD than schizophrenia.

Voxel-based morphometry of patients with schizophrenia or bipolar I disorder: a matched control study

Controlled trials provide critical tests of hypotheses generated by meta-analyses. Two recent meta-analyses have reported that gray matter volumes of schizophrenia and bipolar I patients differ in the amygdala, hippocampus, or perigenual anterior cingulate. The present magnetic resonance imaging study tested these hypotheses in a cross-sectional voxel-based morphometry (VBM) design of 17 chronic schizophrenia and 15 chronic bipolar patients and 21 healthy subjects matched for age, gender and duration of illness. Whole brain gray matter volume of both the schizophrenia and bipolar groups was smaller than among healthy control subjects. Regional voxel-wise comparisons showed that gray matter volume was smallest within frontal and temporal regions of both patient groups. Region of interest analyses found moderately large to large differences between schizophrenia and healthy subjects in the amygdala and hippocampus. There were no group differences in the perigenual anterior cingulate. When schizophrenia and bipolar groups were directly compared, the schizophrenia group showed smaller gray matter volumes in right subcortical regions involving the right hippocampus, putamen, and amygdala. The hippocampal and amygdala findings confirm predictions derived from recent meta-analyses. These structural abnormalities may be important factors in the differential manifestations of these two functional psychotic disorders.

Updated meta-analyses reveal thalamus volume reduction in patients with first-episode and chronic schizophrenia

OBJECTIVES

Although several structural MRI studies report significant thalamus volume reduction in patients with schizophrenia, many other studies do not. Therefore, the present meta-analyses aimed to clarify whether a reduction in thalamic volume characterizes patients diagnosed with schizophrenia by considering first-episode and chronic phases of the illness and right and left thalamus separately.

METHODS

Using Pubmed databases, we made a detailed literature search for structural MRI studies on patients with schizophrenia that reported physical volumetric measures of the right and left thalamus. Thirteen structural MRI studies were considered eligible for meta-analysis of the entire sample of patients and of the healthy control subjects. Individual meta-analyses were also performed on 6 studies of first-episode patients only and on 7 studies of chronic patients only. These were followed by additional meta-analyses to investigate the role of the factors "illness phase" and "side" on thalamic volume reduction.

RESULTS

Overall, the patient group showed a significant bilateral thalamus volume reduction compared to healthy control subjects. This was found in both first-episode and chronic patients. Furthermore, left thalamus was smaller than right in both patients and healthy control subjects.

CONCLUSIONS

When only studies that used physical volumetric measures were considered, the present meta-analyses confirmed that thalamic volume reduction characterizes patients with schizophrenia, both at the first-episode and chronic phases of the illness.

Fully automated analysis using BRAINS: AutoWorkup

The BRAINS (Brain Research: Analysis of Images, Networks, and Systems) image analysis software has been in use, and in constant development, for over 20 years. The original neuroimage analysis pipeline using BRAINS was designed as a semiautomated procedure to measure volumes of the cerebral lobes and subcortical structures, requiring manual intervention at several stages in the process. Through use of advanced image processing algorithms the need for manual intervention at stages of image realignment, tissue sampling, and mask editing have been eliminated. In addition, inhomogeneity correction, intensity normalization, and mask cleaning routines have been added to improve the accuracy and consistency of the results. The fully automated method, AutoWorkup, is shown in this study to be more reliable (ICC ≥ 0.96, Jaccard index ≥ 0.80, and Dice index ≥ 0.89 for all tissues in all regions) than the average of 18 manual raters. On a set of 1130 good quality scans, the failure rate for correct realignment was 1.1%, and manual editing of the brain mask was required on 4% of the scans. In other tests, AutoWorkup is shown to produce measures that are reliable for data acquired across scanners, scanner vendors, and across sequences. Application of AutoWorkup for the analysis of data from the 32-site, multivendor PREDICT-HD study yield estimates of reliability to be greater than or equal to 0.90 for all tissues and regions.

Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy

Quantitative research in neuroimaging often relies on anatomical segmentation of human brain MR images. Recent multi-atlas based approaches provide highly accurate structural segmentations of the brain by propagating manual delineations from multiple atlases in a database to a query subject and combining them. The atlas databases which can be used for these purposes are growing steadily. We present a framework to address the consequent problems of scale in multi-atlas segmentation. We show that selecting a custom subset of atlases for each query subject provides more accurate subcortical segmentations than those given by non-selective combination of random atlas subsets. Using a database of 275 atlases, we tested an image-based similarity criterion as well as a demographic criterion (age) in a leave-one-out cross-validation study. Using a custom ranking of the database for each subject, we combined a varying number n of atlases from the top of the ranked list. The resulting segmentations were compared with manual reference segmentations using Dice overlap. Image-based selection provided better segmentations than random subsets (mean Dice overlap 0.854 vs. 0.811 for the estimated optimal subset size, n=20). Age-based selection resulted in a similar marked improvement. We conclude that selecting atlases from large databases for atlas-based brain image segmentation improves the accuracy of the segmentations achieved. We show that image similarity is a suitable selection criterion and give results based on selecting atlases by age that demonstrate the value of meta-information for selection.

Investigating connectivity between the cerebellum and thalamus in schizophrenia using diffusion tensor tractography: a pilot study

Connections of the cortical-thalamic-cerebellar-cortical regions provide a framework for studying the neural substrates of schizophrenia. A novel diffusion tensor tractography method was used to evaluate the differences in white matter connectivity between 12 patients with schizophrenia and 10 controls. For the tract tracing, we focused on the connection between the cerebellum and the thalamus. Fractional anisotropy (FA) measures along the fiber tracks were compared between patients and the control sample. Fiber tracts located between the cerebellar white matter and the thalamus exhibit a reduced FA in patients with schizophrenia in comparison with controls. The FA values along the defined fiber tracts were not overall reduced but exhibited a reduction in the anisotropy in the region in the superior cerebellar peduncles projecting towards the red nucleus.

Contributions of volumetrics of the hippocampus and thalamus to verbal memory in temporal lobe epilepsy patients

Recent theories have posited that the hippocampus and thalamus serve distinct, yet related, roles in episodic memory. Whereas the hippocampus has been implicated in long-term memory encoding and storage, the thalamus, as a whole, has been implicated in the selection of items for subsequent encoding and the use of retrieval strategies. However, dissociating the memory impairment that occurs following thalamic injury as distinguished from that following hippocampal injury has proven difficult. This study examined relationships between MRI volumetric measures of the hippocampus and thalamus and their contributions to prose and rote verbal memory functioning in 18 patients with intractable temporal lobe epilepsy (TLE). Results revealed that bilateral hippocampal and thalamic volume independently predicted delayed prose verbal memory functioning. However, bilateral hippocampal, but not thalamic, volume predicted delayed rote verbal memory functioning. Follow-up analyses indicated that bilateral thalamic volume independently predicted immediate prose, but not immediate rote, verbal recall, whereas bilateral hippocampal volume was not associated with any of these immediate memory measures. These findings underscore the cognitive significance of thalamic atrophy in chronic TLE, demonstrating that hippocampal and thalamic volume make quantitatively, and perhaps qualitatively, distinct contributions to episodic memory functioning in TLE patients. They are also consistent with theories proposing that the hippocampus supports long-term memory encoding and storage, whereas the thalamus is implicated in the executive aspects of episodic memory.

Automated bony region identification using artificial neural networks: reliability and validation measurements

OBJECTIVE

The objective was to develop tools for automating the identification of bony structures, to assess the reliability of this technique against manual raters, and to validate the resulting regions of interest against physical surface scans obtained from the same specimen.

MATERIALS AND METHODS

Artificial intelligence-based algorithms have been used for image segmentation, specifically artificial neural networks (ANNs). For this study, an ANN was created and trained to identify the phalanges of the human hand.

RESULTS

The relative overlap between the ANN and a manual tracer was 0.87, 0.82, and 0.76, for the proximal, middle, and distal index phalanx bones respectively. Compared with the physical surface scans, the ANN-generated surface representations differed on average by 0.35 mm, 0.29 mm, and 0.40 mm for the proximal, middle, and distal phalanges respectively. Furthermore, the ANN proved to segment the structures in less than one-tenth of the time required by a manual rater.

CONCLUSIONS

The ANN has proven to be a reliable and valid means of segmenting the phalanx bones from CT images. Employing automated methods such as the ANN for segmentation, eliminates the likelihood of rater drift and inter-rater variability. Automated methods also decrease the amount of time and manual effort required to extract the data of interest, thereby making the feasibility of patient-specific modeling a reality.

FreeSurfer-initiated fully-automated subcortical brain segmentation in MRI using Large Deformation Diffeomorphic Metric Mapping

Fully-automated brain segmentation methods have not been widely adopted for clinical use because of issues related to reliability, accuracy, and limitations of delineation protocol. By combining the probabilistic-based FreeSurfer (FS) method with the Large Deformation Diffeomorphic Metric Mapping (LDDMM)-based label-propagation method, we are able to increase reliability and accuracy, and allow for flexibility in template choice. Our method uses the automated FreeSurfer subcortical labeling to provide a coarse-to-fine introduction of information in the LDDMM template-based segmentation resulting in a fully-automated subcortical brain segmentation method (FS+LDDMM). One major advantage of the FS+LDDMM-based approach is that the automatically generated segmentations generated are inherently smooth, thus subsequent steps in shape analysis can directly follow without manual post-processing or loss of detail. We have evaluated our new FS+LDDMM method on several databases containing a total of 50 subjects with different pathologies, scan sequences and manual delineation protocols for labeling the basal ganglia, thalamus, and hippocampus. In healthy controls we report Dice overlap measures of 0.81, 0.83, 0.74, 0.86 and 0.75 for the right caudate nucleus, putamen, pallidum, thalamus and hippocampus respectively. We also find statistically significant improvement of accuracy in FS+LDDMM over FreeSurfer for the caudate nucleus and putamen of Huntington's disease and Tourette's syndrome subjects, and the right hippocampus of Schizophrenia subjects.

Neural bases of dysphoria in early Huntington's disease

Psychiatric disorders, including disorders of emotion control, are common in Huntington's disease. The neurobiological mechanism of the increased rate of disorders of emotion control are not known. Emotion perception deficits have been reported in Huntington's disease, but studies of emotional experience have been limited. In the present study we aim to expand the research in emotion in Huntington's disease by examining the neural bases of induced dysphoria at an early stage of Huntington's disease. Ten Huntington's disease patients and 12 demographically matched healthy volunteers underwent [(15)O] water positron emission tomography while in a transient state of dysphoria induced by viewing negatively charged affect-laden stimuli. Both groups experienced dysphoric mood, but Huntington's disease patients responded to the stimuli with greater arousal, anger and fear than healthy controls. Induced dysphoric mood was associated with a widespread reduction of activity within the frontal and parietal lobes, thalamus, and cerebellum. These differences could not be explained based on the smaller gray matter volumes of the corresponding regions, although in Huntington's disease patients smaller caudate nucleus volumes predicted lower dorsal-lateral prefrontal activity. Areas of increased activity included the striate and extrastriate cortex, the left thalamus, the transverse temporal gyrus, and the posterior hippocampus. This study elucidates possible mechanisms contributing to psychiatric disturbances of emotion often found in patients with Huntington's disease.

Validation of phalanx bone three-dimensional surface segmentation from computed tomography images using laser scanning

OBJECTIVE

To examine the validity of manually defined bony regions of interest from computed tomography (CT) scans.

MATERIALS AND METHODS

Segmentation measurements were performed on the coronal reformatted CT images of the three phalanx bones of the index finger from five cadaveric specimens. Two smoothing algorithms (image-based and Laplacian surface-based) were evaluated to determine their ability to represent accurately the anatomic surface. The resulting surfaces were compared with laser surface scans of the corresponding cadaveric specimen.

RESULTS

The average relative overlap between two tracers was 0.91 for all bones. The overall mean difference between the manual unsmoothed surface and the laser surface scan was 0.20 mm. Both image-based and Laplacian surface-based smoothing were compared; the overall mean difference for image-based smoothing was 0.21 mm and 0.20 mm for Laplacian smoothing.

CONCLUSIONS

This study showed that manual segmentation of high-contrast, coronal, reformatted, CT datasets can accurately represent the true surface geometry of bones. Additionally, smoothing techniques did not significantly alter the surface representations. This validation technique should be extended to other bones, image segmentation and spatial filtering techniques.

Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures

The large amount of imaging data collected in several ongoing multi-center studies requires automated methods to delineate brain structures of interest. We have previously reported on using artificial neural networks (ANN) to define subcortical brain structures. Here we present several automated segmentation methods using multidimensional registration. A direct comparison between template, probability, artificial neural network (ANN) and support vector machine (SVM)-based automated segmentation methods is presented. Three metrics for each segmentation method are reported in the delineation of subcortical and cerebellar brain regions. Results show that the machine learning methods outperform the template and probability-based methods. Utilization of these automated segmentation methods may be as reliable as manual raters and require no rater intervention.

The role of white matter for the pathophysiology of schizophrenia

Inter- and intra-hemispheric connectivity disturbances have been suggested to play a major role in schizophrenia. To this extent, diffusion weighted imaging (DWI) is a relatively new technique examining subtle white matter microstructure organization. DWI studies in schizophrenia strongly suggest that white matter communication is disrupted. This supports the hypothesis that there is a cortico-cortical and transcallosal altered connectivity in schizophrenia, which may be relevant for the pathophysiology and the cognitive disturbances of the disorder. Future longitudinal diffusion and functional imaging studies targeting brain communication together with genetic investigations should further characterize white matter pathology in schizophrenia and its relevance for the development of the illness.

Virtual definition of neuronal tissue by cluster analysis of multi-parametric imaging (virtual-dot-com imaging)

Individual mapping of cerebral, morphological, functionally related structures using MRI was carried out using a new multi-contrast acquisition and analysis framework, called virtual-dot-com imaging. So far, conventional anatomical MRI has been able to provide gross segmentation of gray/white matter boundaries and a few sub-cortical structures. By combining a handful of imaging contrasts mechanisms (T1, T2, magnetization transfer, T2* and proton density), we were able to further segment sub-cortical tissue to its sub-nuclei arrangement, a segmentation that is difficult based on conventional, single-contrast MRI. Using an automatic four-step image and signal processing algorithm, we segmented the thalamus to at least 7 sub-nuclei with high similarity across subjects and high statistical significance within subjects (p<0.0001). The identified sub-nuclei resembled the known anatomical arrangement of the thalamus given in various atlases. Each cluster was characterized by a unique MRI contrast fingerprint. With this procedure, the weighted proportions of the different cellular compartments could be estimated, a property available to date only by histological analysis. Each sub-nucleus could be characterized in terms of normalized MRI contrast and compared to other sub-nuclei. The different weights of the contrasts (T1/T2/T2*/PD/MT, etc.) for each sub-nuclei cluster might indicate the intra-cluster morphological arrangement of the tissue that it represents. The implications of this methodology are far-ranging, from non-invasive, in vivo, individual mapping of histologically distinct brain areas to automatic identification of pathological processes.

Reduced thalamic volume in first-episode non-affective psychosis: correlations with clinical variables, symptomatology and cognitive functioning

Structural studies have inconsistently shown the presence of thalamic volume differences in patients with schizophrenia. However, only a few studies have examined the relation between thalamic structure and clinical and cognitive variables in early phases of the illness. Thalamic volumes in right-handed minimally treated first episode patients with non-affective psychosis (N=61) relative to those of right-handed healthy comparison subjects (N=40) were measured. Thalamic volumes in the right and left hemispheres and total thalamic volume were automatically segmented and analyzed using BRAINS2. Analysis of covariance was used to control for intracranial volume. Clinical symptoms were assessed by total scores of BPRS, SAPS and SANS. The relationship between three cognitive dimensions (verbal learning and memory, speed processing/executive functioning and sustained attention/vigilance), and thalamic volume was evaluated. The impact of the duration of untreated illness, untreated psychosis and prodrome period in thalamic morphometry was also explored. Right, left, and total thalamic volumes of the patients with non-affective psychosis were significantly smaller than those of the healthy subjects. Larger thalamic volumes were associated with an earlier age of onset, a poorer cognitive functioning and a more severe negative symptomatology. Thalamic volumetric differences between patients with non-affective psychosis and healthy controls are already present at early phases of the illness. However, further investigations are warranted to fully clarify the relationship between those structural anomalies and clinical and cognitive outcomes.

Automatic anatomical brain MRI segmentation combining label propagation and decision fusion

Regions in three-dimensional magnetic resonance (MR) brain images can be classified using protocols for manually segmenting and labeling structures. For large cohorts, time and expertise requirements make this approach impractical. To achieve automation, an individual segmentation can be propagated to another individual using an anatomical correspondence estimate relating the atlas image to the target image. The accuracy of the resulting target labeling has been limited but can potentially be improved by combining multiple segmentations using decision fusion. We studied segmentation propagation and decision fusion on 30 normal brain MR images, which had been manually segmented into 67 structures. Correspondence estimates were established by nonrigid registration using free-form deformations. Both direct label propagation and an indirect approach were tested. Individual propagations showed an average similarity index (SI) of 0.754+/-0.016 against manual segmentations. Decision fusion using 29 input segmentations increased SI to 0.836+/-0.009. For indirect propagation of a single source via 27 intermediate images, SI was 0.779+/-0.013. We also studied the effect of the decision fusion procedure using a numerical simulation with synthetic input data. The results helped to formulate a model that predicts the quality improvement of fused brain segmentations based on the number of individual propagated segmentations combined. We demonstrate a practicable procedure that exceeds the accuracy of previous automatic methods and can compete with manual delineations.

Evaluation of the GTRACT diffusion tensor tractography algorithm: A validation and reliability study

Fiber tracking, based on diffusion tensor imaging (DTI), is the only approach available to non-invasively study the three-dimensional structure of white matter tracts. Two major obstacles to this technique are partial volume artifacts and tracking errors caused by image noise. In this paper, a novel fiber tracking algorithm called Guided Tensor Restore Anatomical Connectivity Tractography (GTRACT) is presented. This algorithm utilizes a multi-pass approach to fiber tracking. In the first pass, a 3D graph search algorithm is utilized. The second pass incorporates anatomical connectivity information generated in the first pass to guide the tracking in this stage. This approach improves the ability to reconstruct complex fiber paths as well as the tracking accuracy. Validation and reliability studies using this algorithm were performed on both synthetic phantom data and clinical human brain data. A method is also proposed for the evaluating reliability of fiber tract generation based both on the position of the fiber tracts, as well the anisotropy values along the path. The results demonstrate that the GTRACT algorithm is less sensitive to image noise and more capable of handling areas of complex fiber crossing, compared to conventional streamline methods.

Brain structure in preclinical Huntington's disease

BACKGROUND

Huntington's disease (HD) is traditionally conceptualized as a degenerative disease of the striatum. Recent scientific advances, however, have suggested neurodevelopmental contributions and extrastriatal brain abnormalities. This study was designed to assess the morphology of the brain in participants who had previously undergone elective DNA analyses for the HD mutation who did not currently have a clinical diagnosis of HD (preclinical HD subjects).

METHODS

Twenty-four preclinical participants with the gene expansion for HD underwent brain magnetic resonance imaging and were compared with a group of 24 healthy control subjects, matched by gender and age.

RESULTS

Huntington's disease preclinical participants had substantial morphologic differences from controls throughout the cerebrum. Volume of the cerebral cortex was significantly increased in preclinical HD, whereas the basal ganglia and cerebral white matter volume were substantially decreased.

CONCLUSIONS

In individuals with the HD gene mutation who are considered healthy (preclinical for manifest disease), the morphology of the brain is substantially altered compared with matched control subjects. Although decreased volumes of the striatum and cerebral white matter could represent early degenerative changes, the novel finding of enlarged cortex suggests that developmental pathology occurs in HD.

White matter volume and cognitive dysfunction in early Huntington's disease

BACKGROUND

Structural abnormalities of the striatum and cognitive impairments have consistently been shown in patients with Huntington's disease (HD). Fewer studies have examined other cerebral structures in early HD and potential associations with cognition.

METHOD

Ten patients with early HD and 10 matched control subjects underwent magnetic resonance imaging to provide quantitative measures (volumes) of cortical gray and white matter and the caudate, putamen, and thalamus. Patients completed the Unified Huntington's Disease Rating Scale, including three cognitive tasks.

RESULTS

Although striatal volumes were clearly reduced, white matter was also morphologically abnormal. Cortical gray matter volume was not significantly correlated with cognitive performance. However, the cognitive tasks were most highly correlated with cerebral white matter and, to a lesser degree, striatal volume.

CONCLUSIONS

Cerebral white matter volume may be an important variable to examine in future studies of HD.

Automated segmentation of neuroanatomical structures in multispectral MR microscopy of the mouse brain

We present the automated segmentation of magnetic resonance microscopy (MRM) images of the C57BL/6J mouse brain into 21 neuroanatomical structures, including the ventricular system, corpus callosum, hippocampus, caudate putamen, inferior colliculus, internal capsule, globus pallidus, and substantia nigra. The segmentation algorithm operates on multispectral, three-dimensional (3D) MR data acquired at 90-microm isotropic resolution. Probabilistic information used in the segmentation is extracted from training datasets of T2-weighted, proton density-weighted, and diffusion-weighted acquisitions. Spatial information is employed in the form of prior probabilities of occurrence of a structure at a location (location priors) and the pairwise probabilities between structures (contextual priors). Validation using standard morphometry indices shows good consistency between automatically segmented and manually traced data. Results achieved in the mouse brain are comparable with those achieved in human brain studies using similar techniques. The segmentation algorithm shows excellent potential for routine morphological phenotyping of mouse models.

N-acetylaspartate reductions in the mediodorsal and anterior thalamus in men with schizophrenia verified by tissue volume corrected proton MRSI

OBJECTIVE

Deficits in the mediodorsal and anterior nuclei of the thalamus may contribute to the psychopathological symptoms of schizophrenia. These thalamic nuclei have been found to be abnormal in schizophrenia and have close connections with other brain structures implicated in the disorder. We therefore examined schizophrenia-related alterations in brain metabolite levels specifically in the mediodorsal and anterior thalamic subregions.

METHOD

We used in vivo proton magnetic resonance spectroscopic imaging ((1)H MRSI) to measure N-acetylaspartate (NAA), choline-containing compounds (Cho), and creatine+phosphocreatine (Cr) in the mediodorsal and anterior thalamus in 22 male patients with schizophrenia and 22 male controls. Magnetic resonance imaging (MRI) tissue segmentation and thalamic volume mask techniques were performed to distinguish the thalamus, extrathalamic gray and white matter, and CSF within the spectroscopic voxels.

RESULTS

Compared to healthy subjects, patients with schizophrenia had significantly lower NAA in the mediodorsal and anterior thalamus bilaterally. No significant differences in Cho or Cr levels were seen. NAA was significantly higher in the left thalamus relative to the right in both groups. We found a strong negative correlation between left thalamic NAA and duration of illness, even after partialling out the effect of age. Tissue segmentation and thalamic volume mask techniques detected no group or lateralized differences in tissue type or CSF percentages, demonstrating that the metabolite reductions were not an artifact of spectroscopic voxel heterogeneity.

CONCLUSIONS

These findings suggest diminished function and/or structure in the mediodorsal and anterior thalamus in male patients with schizophrenia and support earlier research demonstrating schizophrenia-related abnormalities in the thalamus and its circuitry.

Magnetic resonance findings in bipolar disorder

The MR findings reviewed in this article suggest structural, chemical, and functional abnormalities in specific brain regions participating in mood and cognitive regulation, such as the DLPFC, anterior cingulate, amygdala,STG, and corpus callosum in subjects with bipolar disorder. These abnormalities would represent an altered anterior-limbic network disrupting inter- and intrahemispheric communication and underlying the expression of bipolar disorder. Available studies are limited by several confounding variables, such as small and heterogeneous patient samples, differences in clinical and medication status, and cross-sectional design. It is still unclear whether abnormalities in neurodevelopment or neurodegeneration play a major role in the pathophysiology of bipolar disorder. These processes could act together in a unitary model of the disease, with excessive neuronal pruning/apoptosis during childhood and adolescence being responsible for the onset of the disorder and subsequent neurotoxic mechanisms and impaired neuroplasticity and cellular resilience being responsible for further disease progression. Future MR studies should investigate larger samples of first-episode drug-free patients, pediatric patients, subjects at high risk for bipolar disorder, and unaffected family members longitudinally. Such a study population is crucial to examine systematically whether brain changes are present before the appearance of symptoms (eg, maldevelopment) or whether they develop afterwards, as a result of illness course (eg, neurodegeneration). These studies will also be instrumental in minimizing potentially confounding factors commonly found in adult samples, such as the effects of long-term medication, chronicity, and hospitalizations. Juvenile bipolar patients often have a strong family history of bipolar disorder. Future studies could help elucidate the relevance of brain abnormalities as reflections of genetic susceptibility to the disorder. MR studies associated with genetic, post-mortem, and neuropsychologic studies will be valuable in separating state from trait brain abnormalities and in further characterizing the genetic determinants, the neuropathologic underpinnings, and the cognitive disturbances of bipolar disorder.

Morphometry demonstrates loss of cortical thickness in cerebral microangiopathy

OBJECTIVE

To evaluate the role of MR morphometry in the characterization of cerebral microangiopathy (CMA) in relation to clinical and neuropsychological impairment.

SUBJECTS AND METHODS

3D MR images of 27 patients and 27 age-matched controls were morphometrically analysed for regional thickness. The normalized values were related to the patients' clinical and neuropsychological scores. The patients were categorised according to the amount of structural MR signal changes. A ventricle index reflecting internal atrophy was related to MR morphology and cortical thickness as an indicator for external atrophy.

RESULTS

Cortical thickness was significantly reduced in the patients group (3.03 mm +/- 0.26 vs. 3.22 mm +/-0.13 in controls, p=0.001). The severest loss of cortical thickness occurred in severe CMA. Internal and external atrophy evolved in parallel and both showed a significant relationship with structural MR-abnormalities (p<0.05; r=-0.7; r=0.67; r=-0.74, respectively). Neuropsychological performance correlated strongly with the loss of cortical thickness.

CONCLUSIONS

Cortical thickness was identified as the most sensitive parameter to characterize CMA. A strong correlation was found of morphometric parameters to the severity of CMA based on a score derived from T2-weighted MRI. The degree of cortical atrophy was directly related to the degree of neuropsychological impairment. Our findings suggest that the cortical thickness is a valid marker in the structural and clinical characterization of CMA.

Subcortical, cerebellar, and magnetic resonance based consistent brain image registration

A new landmark-initialized segmentation and intensity-based (LI-SI) inverse-consistent linear elastic image registration algorithm is presented. This method uses manually identified landmarks, segmented volumetric (anatomical) structures, and normalized image signal intensity information to coregister datasets. The features used for image registration and evaluation include 35 cortical, cerebellar, and commissure landmarks manually identified by experts, subcortical and cerebellar regions defined semi-automatically by an artificial neural network and manually trimmed for validity by experts, and tissue classified images that were generated using a discriminant analysis of three magnetic resonance image sets representing T1, T2, and PD modalities. Four groups of results were computed for coregistering 16 datasets with the following registration techniques: rigid registration, extended Talairach registration, intensity-only inverse-consistent linear elastic registration, and the new LI-SI registration. Results are presented showing that relative overlap measurements increased as the dimensionality of the registration algorithm and amount of anatomical information increased. The average relative overlap improved from 0.53 for the rigid registration to 0.55 for the Talairach registration to 0.74 for the intensity-only and to 0.85 for the LI-SI algorithm. We showed a statistically significant improvement for all but one structure using the intensity-only algorithm compared to the Talairach registration. Furthermore, statistically significant improvements for all structures were achieved using the LI-SI algorithm compared to the intensity-only algorithm.