A robust method for extraction and automatic segmentation of brain images

Perivascular spaces, plasma GFAP, and speeded executive function in neurodegenerative diseases

INTRODUCTION

We investigated the effect of perivascular spaces (PVS) volume on speeded executive function (sEF), as mediated by white matter hyperintensities (WMH) volume and plasma glial fibrillary acidic protein (GFAP) in neurodegenerative diseases.

METHODS

A mediation analysis was performed to assess the relationship between neuroimaging markers and plasma biomarkers on sEF in 333 participants clinically diagnosed with Alzheimer's disease/mild cognitive impairment, frontotemporal dementia, or cerebrovascular disease from the Ontario Neurodegenerative Disease Research Initiative.

RESULTS

PVS was significantly associated with sEF (c = -0.125 ± 0.054, 95% bootstrap confidence interval [CI] [-0.2309, -0.0189], p = 0.021). This effect was mediated by both GFAP and WMH.

DISCUSSION

In this unique clinical cohort of neurodegenerative diseases, we demonstrated that the effect of PVS on sEF was mediated by the presence of elevated plasma GFAP and white matter disease. These findings highlight the potential utility of imaging and plasma biomarkers in the current landscape of therapeutics targeting dementia.

HIGHLIGHTS

Perivascular spaces (PVS) and white matter hyperintensities (WMH) are imaging markers of small vessel disease. Plasma glial fibrillary protein acidic protein (GFAP) is a biomarker of astroglial injury. PVS, WMH, and GFAP are relevant in executive dysfunction from neurodegeneration. PVS's effect on executive function was mediated by GFAP and white matter disease.

Link among apolipoprotein E E4, gait, and cognition in neurodegenerative diseases: ONDRI study

INTRODUCTION

Apolipoprotein E E4 allele (APOE E4) and slow gait are independently associated with cognitive impairment and dementia. However, it is unknown whether their coexistence is associated with poorer cognitive performance and its underlying mechanism in neurodegenerative diseases.

METHODS

Gait speed, APOE E4, cognition, and neuroimaging were assessed in 480 older adults with neurodegeneration. Participants were grouped by APOE E4 presence and slow gait. Mediation analyses were conducted to determine if brain structures could explain the link between these factors and cognitive performance.

RESULTS

APOE E4 carriers with slow gait had the lowest global cognitive performance and smaller gray matter volumes compared to non-APOE E4 carriers with normal gait. Coexistence of APOE E4 and slow gait best predicted global and domain-specific poorer cognitive performances, mediated by smaller gray matter volume.

DISCUSSION

Gait slowness in APOE E4 carriers with neurodegenerative diseases may indicate extensive gray matter changes associated with poor cognition.

HIGHLIGHTS

APOE E4 and slow gait are risk factors for cognitive decline in neurodegenerative diseases. Slow gait and smaller gray matter volumes are associated, independently of APOE E4. Worse cognition in APOE E4 carriers with slow gait is explained by smaller GM volume. Gait slowness in APOE E4 carriers indicates poorer cognition-related brain changes.

White matter hyperintensities and smaller cortical thickness are associated with neuropsychiatric symptoms in neurodegenerative and cerebrovascular diseases

BACKGROUND

Neuropsychiatric symptoms (NPS) are a core feature of most neurodegenerative and cerebrovascular diseases. White matter hyperintensities and brain atrophy have been implicated in NPS. We aimed to investigate the relative contribution of white matter hyperintensities and cortical thickness to NPS in participants across neurodegenerative and cerebrovascular diseases.

METHODS

Five hundred thirteen participants with one of these conditions, i.e. Alzheimer's Disease/Mild Cognitive Impairment, Amyotrophic Lateral Sclerosis, Frontotemporal Dementia, Parkinson's Disease, or Cerebrovascular Disease, were included in the study. NPS were assessed using the Neuropsychiatric Inventory - Questionnaire and grouped into hyperactivity, psychotic, affective, and apathy subsyndromes. White matter hyperintensities were quantified using a semi-automatic segmentation technique and FreeSurfer cortical thickness was used to measure regional grey matter loss.

RESULTS

Although NPS were frequent across the five disease groups, participants with frontotemporal dementia had the highest frequency of hyperactivity, apathy, and affective subsyndromes compared to other groups, whilst psychotic subsyndrome was high in both frontotemporal dementia and Parkinson's disease. Results from univariate and multivariate results showed that various predictors were associated with neuropsychiatric subsyndromes, especially cortical thickness in the inferior frontal, cingulate, and insula regions, sex(female), global cognition, and basal ganglia-thalamus white matter hyperintensities.

CONCLUSIONS

In participants with neurodegenerative and cerebrovascular diseases, our results suggest that smaller cortical thickness and white matter hyperintensity burden in several cortical-subcortical structures may contribute to the development of NPS. Further studies investigating the mechanisms that determine the progression of NPS in various neurodegenerative and cerebrovascular diseases are needed.

Methods of Brain Extraction from Magnetic Resonance Images of Human Head: A Review

Medical images are providing vital information to aid physicians in diagnosing a disease afflicting the organ of a human body. Magnetic resonance imaging is an important imaging modality in capturing the soft tissues of the brain. Segmenting and extracting the brain is essential in studying the structure and pathological condition of brain. There are several methods that are developed for this purpose. Researchers in brain extraction or segmentation need to know the current status of the work that have been done. Such an information is also important for improving the existing method to get more accurate results or to reduce the complexity of the algorithm. In this paper we review the classical methods and convolutional neural network-based deep learning brain extraction methods.

Comparison of Diffusion Tensor Imaging Metrics in Normal-Appearing White Matter to Cerebrovascular Lesions and Correlation with Cerebrovascular Disease Risk Factors and Severity

Alterations in tissue microstructure in normal-appearing white matter (NAWM), specifically measured by diffusion tensor imaging (DTI) fractional anisotropy (FA), have been associated with cognitive outcomes following stroke. The purpose of this study was to comprehensively compare conventional DTI measures of tissue microstructure in NAWM to diverse vascular brain lesions in people with cerebrovascular disease (CVD) and to examine associations between FA in NAWM and cerebrovascular risk factors. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were measured in cerebral tissues and cerebrovascular anomalies from 152 people with CVD participating in the Ontario Neurodegenerative Disease Research Initiative (ONDRI). Ten cerebral tissue types were segmented including NAWM, and vascular lesions including stroke, periventricular and deep white matter hyperintensities, periventricular and deep lacunar infarcts, and perivascular spaces (PVS) using T1-weighted, proton density-weighted, T2-weighted, and fluid attenuated inversion recovery MRI scans. Mean DTI metrics were measured in each tissue region using a previously developed DTI processing pipeline and compared between tissues using multivariate analysis of covariance. Associations between FA in NAWM and several CVD risk factors were also examined. DTI metrics in vascular lesions differed significantly from healthy tissue. Specifically, all tissue types had significantly different MD values, while FA was also found to be different in most tissue types. FA in NAWM was inversely related to hypertension and modified Rankin scale (mRS). This study demonstrated the differences between conventional DTI metrics, FA, MD, AD, and RD, in cerebral vascular lesions and healthy tissue types. Therefore, incorporating DTI to characterize the integrity of the tissue microstructure could help to define the extent and severity of various brain vascular anomalies. The association between FA within NAWM and clinical evaluation of hypertension and disability provides further evidence that white matter microstructural integrity is impacted by cerebrovascular function.

Cognitive and Neuroimaging Profiles of Older Adults With Attention Deficit/Hyperactivity Disorder Presenting to a Memory Clinic

OBJECTIVE

Some features of attention-deficit/hyperactivity disorder (ADHD) may resemble those of mild cognitive impairment (MCI) in older adults, contributing to diagnostic uncertainty in individuals seeking assessment in memory clinics. We systematically compared cognition and brain structure in ADHD and MCI to clarify the extent of overlap and identify potential features unique to each.

METHOD

Older adults from a Cognitive Neurology clinic (40 ADHD, 29 MCI, 37 controls) underwent neuropsychological assessment. A subsample (n = 80) underwent structural neuroimaging.

RESULTS

Memory was impaired in both patient groups, but reflected a storage deficit in MCI (supported by relatively smaller hippocampi) and an encoding deficit in ADHD (supported by frontal lobe thinning). Both groups displayed normal executive functioning. Semantic retrieval was uniquely impaired in MCI.

CONCLUSION

Although ADHD has been proposed as a dementia risk factor or prodrome, we propose it is rather a pathophysiologically-unique phenotypic mimic acting via overlap in memory and executive performance.

Investigating the contribution of white matter hyperintensities and cortical thickness to empathy in neurodegenerative and cerebrovascular diseases

Change in empathy is an increasingly recognised symptom of neurodegenerative diseases and contributes to caregiver burden and patient distress. Empathy impairment has been associated with brain atrophy but its relationship to white matter hyperintensities (WMH) is unknown. We aimed to investigate the relationships amongst WMH, brain atrophy, and empathy deficits in neurodegenerative and cerebrovascular diseases. Five hundred thirteen participants with Alzheimer's disease/mild cognitive impairment, amyotrophic lateral sclerosis, frontotemporal dementia (FTD), Parkinson's disease, or cerebrovascular disease (CVD) were included. Empathy was assessed using the Interpersonal Reactivity Index. WMH were measured using a semi-automatic segmentation and FreeSurfer was used to measure cortical thickness. A heterogeneous pattern of cortical thinning was found between groups, with FTD showing thinning in frontotemporal regions and CVD in left superior parietal, left insula, and left postcentral. Results from both univariate and multivariate analyses revealed that several variables were associated with empathy, particularly cortical thickness in the fronto-insulo-temporal and cingulate regions, sex (female), global cognition, and right parietal and occipital WMH. Our results suggest that cortical atrophy and WMH may be associated with empathy deficits in neurodegenerative and cerebrovascular diseases. Future work should consider investigating the longitudinal effects of WMH and atrophy on empathy deficits in neurodegenerative and cerebrovascular diseases.

Neuropsychiatric symptoms as a sign of small vessel disease progression in cognitive impairment

Background

Neuropsychiatric symptoms associate cross-sectionally with cerebral small vessel disease but it is not clear whether these symptoms could act as early clinical markers of small vessel disease progression. We investigated whether longitudinal change in Neuropsychiatric Inventory (NPI) scores associated with white matter hyperintensity (WMH) progression in a memory clinic population.

Material and methods

We included participants from the prospective Sunnybrook Dementia Study with Alzheimer's disease and vascular subtypes of mild cognitive impairment and dementia with two MRI and ≥ 1 NPI. We conducted linear mixed-effects analyses, adjusting for age, atrophy, vascular risk factors, cognition, function, and interscan interval.

Results

At baseline (n=124), greater atrophy, age, vascular risk factors and total NPI score were associated with higher baseline WMH volume, while longitudinally, all but vascular risk factors were associated. Change in total NPI score was associated with change in WMH volume, χ2 = 7.18, p = 0.007, whereby a one-point change in NPI score from baseline to follow-up was associated with a 0.0017 change in normalized WMH volume [expressed as cube root of (WMH volume cm³ as % intracranial volume)], after adjusting for age, atrophy, vascular risk factors and interscan interval.

Conclusions

In memory clinic patients, WMH progression over 1-2 years associated with worsening neuropsychiatric symptoms, while WMH volume remained unchanged in those with stable NPI scores in this population with low background WMH burden.

Cortical Thickness Estimation in Individuals With Cerebral Small Vessel Disease, Focal Atrophy, and Chronic Stroke Lesions

BACKGROUND

Regional changes to cortical thickness in individuals with neurodegenerative and cerebrovascular diseases (CVD) can be estimated using specialized neuroimaging software. However, the presence of cerebral small vessel disease, focal atrophy, and cortico-subcortical stroke lesions, pose significant challenges that increase the likelihood of misclassification errors and segmentation failures.

PURPOSE

The main goal of this study was to examine a correction procedure developed for enhancing FreeSurfer's (FS's) cortical thickness estimation tool, particularly when applied to the most challenging MRI obtained from participants with chronic stroke and CVD, with varying degrees of neurovascular lesions and brain atrophy.

METHODS

In 155 CVD participants enrolled in the Ontario Neurodegenerative Disease Research Initiative (ONDRI), FS outputs were compared between a fully automated, unmodified procedure and a corrected procedure that accounted for potential sources of error due to atrophy and neurovascular lesions. Quality control (QC) measures were obtained from both procedures. Association between cortical thickness and global cognitive status as assessed by the Montreal Cognitive Assessment (MoCA) score was also investigated from both procedures.

RESULTS

Corrected procedures increased "Acceptable" QC ratings from 18 to 76% for the cortical ribbon and from 38 to 92% for tissue segmentation. Corrected procedures reduced "Fail" ratings from 11 to 0% for the cortical ribbon and 62 to 8% for tissue segmentation. FS-based segmentation of T1-weighted white matter hypointensities were significantly greater in the corrected procedure (5.8 mL vs. 15.9 mL, p < 0.001). The unmodified procedure yielded no significant associations with global cognitive status, whereas the corrected procedure yielded positive associations between MoCA total score and clusters of cortical thickness in the left superior parietal (p = 0.018) and left insula (p = 0.04) regions. Further analyses with the corrected cortical thickness results and MoCA subscores showed a positive association between left superior parietal cortical thickness and Attention (p < 0.001).

CONCLUSION

These findings suggest that correction procedures which account for brain atrophy and neurovascular lesions can significantly improve FS's segmentation results and reduce failure rates, thus maximizing power by preventing the loss of our important study participants. Future work will examine relationships between cortical thickness, cerebral small vessel disease, and cognitive dysfunction due to neurodegenerative disease in the ONDRI study.

Ontario Neurodegenerative Disease Research Initiative (ONDRI): Structural MRI Methods and Outcome Measures

The Ontario Neurodegenerative Research Initiative (ONDRI) is a 3 years multi-site prospective cohort study that has acquired comprehensive multiple assessment platform data, including 3T structural MRI, from neurodegenerative patients with Alzheimer's disease, mild cognitive impairment, Parkinson's disease, amyotrophic lateral sclerosis, frontotemporal dementia, and cerebrovascular disease. This heterogeneous cross-section of patients with complex neurodegenerative and neurovascular pathologies pose significant challenges for standard neuroimaging tools. To effectively quantify regional measures of normal and pathological brain tissue volumes, the ONDRI neuroimaging platform implemented a semi-automated MRI processing pipeline that was able to address many of the challenges resulting from this heterogeneity. The purpose of this paper is to serve as a reference and conceptual overview of the comprehensive neuroimaging pipeline used to generate regional brain tissue volumes and neurovascular marker data that will be made publicly available online.

Comparison of quality control methods for automated diffusion tensor imaging analysis pipelines

The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data.

Speech network regional involvement in bulbar ALS: a multimodal structural MRI study

Objective: To examine gray (GM) and white matter (WM) structural changes in regions of the speech network (SpN) in ALS patients with varying degree of bulbar disease. Methods: T1 and DTI images were obtained for 19 ALS participants and 13 neurologically-intact controls. Surface-based, volumetric, and DTI metrics were obtained for 6 regions-of-interest (ROIs) including the primary motor cortex (PMC), pars triangularis (parsT), pars opercularis (ParsO), posterior superior temporal gyrus (pSTG), and transverse temporal (TT). Disease-effects and brain-behavioral correlates between neuroanatomy and clinical measures of bulbar, limb, and overall disability were examined using linear models. Results: Structural changes were observed in the right oral and limb PMC and left ParsT, TT, and pSTG in ALS. Bulbar motor dysfunction was associated with WM abnormalities in the right oral PMC and left pSTG, and GM changes in bilateral TT. In contrast, symptom progression rate predicted GM and WM changes in bilateral pars opercularis (part of Broca's area). Grip strength and disease duration models were non-significant. Conclusions: The findings suggested that regions of the left-dominant SpN may be implicated in ALS and degeneration of these areas are related to bulbar disease severity. Involvement of regions that overlap across multiple connectomes such as Broca's area, however, may be dependent on the rate of disease progression. The work contributes to our understanding of bulbar ALS subtype, which is crucial for predicting disease progression, delivering targeted clinical care, and appropriate recruitment into clinical trials.

Frontal Anatomical Correlates of Cognitive and Speech Motor Deficits in Amyotrophic Lateral Sclerosis

The goal of this study was to identify neurostructural frontal lobe correlates of cognitive and speaking rate changes in amyotrophic lateral sclerosis (ALS). 17 patients diagnosed with ALS and 12 matched controls underwent clinical, bulbar, and neuropsychological assessment and structural neuroimaging. Neuropsychological testing was performed via a novel computerized frontal battery (ALS-CFB), based on a validated theoretical model of frontal lobe functions, and focused on testing energization, executive function, emotion processing, theory of mind, and behavioral inhibition via antisaccades. The measure of speaking rate represented bulbar motor changes. Neuroanatomical assessment was performed using volumetric analyses focused on frontal lobe regions, postcentral gyrus, and occipital lobes as controls. Partial least square regressions (PLS) were used to predict behavioral (cognitive and speech rate) outcomes using volumetric measures. The data supported the overall hypothesis that distinct behavioral changes in cognition and speaking rate in ALS were related to specific regional neurostructural brain changes. These changes did not support a notion of a general dysexecutive syndrome in ALS. The observed specificity of behavior-brain changes can begin to provide a framework for subtyping of ALS. The data also support a more integrative framework for clinical assessment of frontal lobe functioning in ALS, which requires both behavioral testing and neuroimaging.

APOE-ε4 associates with hippocampal volume, learning, and memory across the spectrum of Alzheimer's disease and dementia with Lewy bodies

INTRODUCTION

Although the apolipoprotein E ε4-allele (APOE-ε4) is a susceptibility factor for Alzheimer's disease (AD) and dementia with Lewy bodies (DLB), its relationship with imaging and cognitive measures across the AD/DLB spectrum remains unexplored.

METHODS

We studied 298 patients (AD = 250, DLB = 48; 38 autopsy-confirmed; NCT01800214) using neuropsychological testing, volumetric magnetic resonance imaging, and APOE genotyping to investigate the association of APOE-ε4 with hippocampal volume and learning/memory phenotypes, irrespective of diagnosis.

RESULTS

Across the AD/DLB spectrum: (1) hippocampal volumes were smaller with increasing APOE-ε4 dosage (no genotype × diagnosis interaction observed), (2) learning performance as assessed by total recall scores was associated with hippocampal volumes only among APOE-ε4 carriers, and (3) APOE-ε4 carriers performed worse on long-delay free word recall.

DISCUSSION

These findings provide evidence that APOE-ε4 is linked to hippocampal atrophy and learning/memory phenotypes across the AD/DLB spectrum, which could be useful as biomarkers of disease progression in therapeutic trials of mixed disease.

The effect of focal cortical frontal and posterior lesions on recollection and familiarity in recognition memory

Recognition memory can be subdivided into two processes: recollection (a contextually rich memory) and familiarity (a sense that an item is old). The brain network supporting recognition encompasses frontal, parietal and medial temporal regions. Which specific regions within the frontal lobe are critical for recollection vs. familiarity, however, are unknown; past studies of focal lesion patients have yielded conflicting results. We examined patients with focal lesions confined to medial polar (MP), right dorsal frontal (RDF), right frontotemporal (RFT), left dorsal frontal (LDF), temporal, and parietal regions and matched controls. A series of words and their humorous definitions were presented either auditorily or visually to all participants. Recall, recognition, and source memory were tested at 30 min and 24 h delay, along with "remember/know" judgments for recognized items. The MP, RDF, temporal and parietal groups were impaired on subjectively reported recollection; their intact recognition performance was supported by familiarity. None of the groups were impaired on cued recall, recognition familiarity or source memory. These findings suggest that the MP and RDF regions, along with parietal and temporal regions, are necessary for subjectively-reported recollection, while the LDF and right frontal ventral regions, as those affected in the RTF group, are not.

Small vessel disease is linked to disrupted structural network covariance in Alzheimer's disease

INTRODUCTION

Cerebral small vessel disease (SVD) is thought to contribute to Alzheimer's disease (AD) through abnormalities in white matter networks. Gray matter (GM) hub covariance networks share only partial overlap with white matter connectivity, and their relationship with SVD has not been examined in AD.

METHODS

We developed a multivariate analytical pipeline to elucidate the cortical GM thickness systems that covary with major network hubs and assessed whether SVD and neurodegenerative pathologic markers were associated with attenuated covariance network integrity in mild AD and normal elderly control subjects.

RESULTS

SVD burden was associated with reduced posterior cingulate corticocortical GM network integrity and subneocorticocortical hub network integrity in AD.

DISCUSSION

These findings provide evidence that SVD is linked to the selective disruption of cortical hub GM networks in AD brains and point to the need to consider GM hub covariance networks when assessing network disruption in mixed disease.

Evaluation of Spontaneous Spinal Cerebrospinal Fluid Leaks Disease by Computerized Image Processing

BACKGROUND

Spontaneous Spinal Cerebrospinal Fluid Leaks (SSCFL) is a disease based on tears on the dura mater. Due to widespread symptoms and low frequency of the disease, diagnosis is problematic. Diagnostic lumbar puncture is commonly used for diagnosing SSCFL, though it is invasive and may cause pain, inflammation or new leakages. T2-weighted MR imaging is also used for diagnosis; however, the literature on T2-weighted MRI states that findings for diagnosis of SSCFL could be erroneous when differentiating the diseased and control. One another technique for diagnosis is CT-myelography, but this has been suggested to be less successful than T2-weighted MRI and it needs an initial lumbar puncture.

OBJECTIVES

This study aimed to develop an objective, computerized numerical analysis method using noninvasive routine Magnetic Resonance Images that can be used in the evaluation and diagnosis of SSCFL disease.

METHODS

Brain boundaries were automatically detected using methods of mathematical morphology, and a distance transform was employed. According to normalized distances, average densities of certain sites were proportioned and a numerical criterion related to cerebrospinal fluid distribution was calculated.

RESULTS

The developed method was able to differentiate between 14 patients and 14 control subjects significantly with p = 0.0088 and d = 0.958. Also, the pre and post-treatment MRI of four patients was obtained and analyzed. The results were differentiated statistically (p = 0.0320, d = 0.853).

CONCLUSIONS

An original, noninvasive and objective diagnostic test based on computerized image processing has been developed for evaluation of SSCFL. To our knowledge, this is the first computerized image processing method for evaluation of the disease. Discrimination between patients and controls shows the validity of the method. Also, post-treatment changes observed in four patients support this verdict.

Dynamic Progression of White Matter Hyperintensities in Alzheimer's Disease and Normal Aging: Results from the Sunnybrook Dementia Study

Although white matter hyperintensities (WMH), markers of cerebral small vessel disease (SVD), are believed to generally increase over time, some studies have shown sharp decreases after therapeutic intervention, suggesting that WMH progression may be more dynamic than previously thought. Our primary goal was to examine dynamic progression of WMH in a real-world sample of Alzheimer’s disease (AD) patients and normal elderly (NC), with varying degrees of SVD. WMH volumes from serial magnetic resonance imaging (MRI; mean = 1.8 years) were measured from NC (n = 44) and AD patients (n = 113) with high and low SVD burden. Dynamic progression for each individual was measured using spatial overlap images to assess shrinkage, growth, and stable WMH volumes. Significant group differences were found for shrinkage (p < 0.001), growth (p < 0.001) and stable (p < 0.001) WMH, where the AD high SVD group showed the largest changes relative to low SVD and NC. Our results suggest spatial progression measured at the individual patient level may be more sensitive to the dynamic nature of WMH.

Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review

White matter hyperintensities (WMH) are commonly seen in the brain of healthy elderly subjects and patients with several neurological and vascular disorders. A truly reliable and fully automated method for quantitative assessment of WMH on magnetic resonance imaging (MRI) has not yet been identified. In this paper, we review and compare the large number of automated approaches proposed for segmentation of WMH in the elderly and in patients with vascular risk factors. We conclude that, in order to avoid artifacts and exclude the several sources of bias that may influence the analysis, an optimal method should comprise a careful preprocessing of the images, be based on multimodal, complementary data, take into account spatial information about the lesions and correct for false positives. All these features should not exclude computational leanness and adaptability to available data.

Differentiating between visual hallucination-free dementia with Lewy bodies and corticobasal syndrome on the basis of neuropsychology and perfusion single-photon emission computed tomography

INTRODUCTION

Dementia with Lewy bodies (DLB) and Corticobasal Syndrome (CBS) are atypical parkinsonian disorders with fronto-subcortical and posterior cognitive dysfunction as common features. While visual hallucinations are a good predictor of Lewy body pathology and are rare in CBS, they are not exhibited in all cases of DLB. Given the clinical overlap between these disorders, neuropsychological and imaging markers may aid in distinguishing these entities.

METHODS

Prospectively recruited case-control cohorts of CBS (n =31) and visual hallucination-free DLB (n =30), completed neuropsychological and neuropsychiatric measures as well as brain perfusion single-photon emission computed tomography and structural magnetic resonance imaging (MRI). Perfusion data were available for forty-two controls. Behavioural, perfusion, and cortical volume and thickness measures were compared between the groups to identify features that serve to differentiate them.

RESULTS

The Lewy body with no hallucinations group performed more poorly on measures of episodic memory compared to the corticobasal group, including the delayed and cued recall portions of the California Verbal Learning Test (F (1, 42) =23.1, P <0.001 and F (1, 42) =14.0, P =0.001 respectively) and the delayed visual reproduction of the Wechsler Memory Scale-Revised (F (1, 36) =9.7, P =0.004). The Lewy body group also demonstrated reduced perfusion in the left occipital pole compared to the corticobasal group (F (1,57) =7.4, P =0.009). At autopsy, the Lewy body cases all demonstrated mixed dementia with Lewy bodies, Alzheimer's disease and small vessel arteriosclerosis, while the corticobasal cases demonstrated classical corticobasal degeneration in five, dementia with agyrophilic grains + corticobasal degeneration + cerebral amyloid angiopathy in one, Progressive Supranuclear Palsy in two, and Frontotemporal Lobar Degeneration-Ubiquitin/TAR DNA-binding protein 43 proteinopathy in one. MRI measures were not significantly different between the patient groups.

CONCLUSIONS

Reduced perfusion in the left occipital region and worse episodic memory performance may help to distinguish between DLB cases who have never manifested with visual hallucinations and CBS at earlier stages of the disease. Development of reliable neuropsychological and imaging markers that improve diagnostic accuracy will become increasingly important as disease modifying therapies become available.

Subcortical hyperintensity volumetrics in Alzheimer's disease and normal elderly in the Sunnybrook Dementia Study: correlations with atrophy, executive function, mental processing speed, and verbal memory

INTRODUCTION

Subcortical hyperintensities (SHs) are radiological entities commonly observed on magnetic resonance imaging (MRI) of patients with Alzheimer's disease (AD) and normal elderly controls. Although the presence of SH is believed to indicate some form of subcortical vasculopathy, pathological heterogeneity, methodological differences, and the contribution of brain atrophy associated with AD pathology have yielded inconsistent results in the literature.

METHODS

Using the Lesion Explorer (LE) MRI processing pipeline for SH quantification and brain atrophy, this study examined SH volumes of interest and cognitive function in a sample of patients with AD (n = 265) and normal elderly controls (n = 100) from the Sunnybrook Dementia Study.

RESULTS

Compared with healthy controls, patients with AD were found to have less gray matter, less white matter, and more sulcal and ventricular cerebrospinal fluid (all significant, P <0.0001). Additionally, patients with AD had greater volumes of whole-brain SH (P <0.01), periventricular SH (pvSH) (P <0.01), deep white SH (dwSH) (P <0.05), and lacunar lesions (P <0.0001). In patients with AD, regression analyses revealed a significant association between global atrophy and pvSH (P = 0.02) and ventricular atrophy with whole-brain SH (P <0.0001). Regional volumes of interest revealed significant correlations with medial middle frontal SH volume and executive function (P <0.001) in normal controls but not in patients with AD, global pvSH volume and mental processing speed (P <0.01) in patients with AD, and left temporal SH volume and memory (P <0.01) in patients with AD.

CONCLUSIONS

These brain-behavior relationships and correlations with brain atrophy suggest that subtle, yet measurable, signs of small vessel disease may have potential clinical relevance as targets for treatment in Alzheimer's dementia.

Object alternation: a novel probe of medial frontal function in frontotemporal dementia

We studied behavioral variant frontotemporal dementia (bvFTD) using object alternation (OA) as a novel probe of cognition. This task was adopted from animal models and is sensitive to ventrolateral-orbitofrontal and medial frontal function in humans. OA was administered to bvFTD patients, normal controls, and a dementia control group with Alzheimer disease (AD). Two other frontal lobe measures adopted from animal models were administered: delayed response (DR) and delayed alternation (DA). Brain volumes were measured using the semiautomatic brain region extraction method. Compared with the normal controls, bvFTD patients were significantly impaired on OA and DR. For OA and DR, sensitivities and specificities were 100% and 51.5% (cutoff=22.5 errors) and 9.5% and 98% (cutoff=1.5 errors), respectively. Negative predictive value (NPV) for OA was 100% at all prevalence rates. Comparing AD with bvFTD, there were no significant differences on OA, DR, or DA. Nevertheless, positive predictive value (PPV) and NPV were good at all prevalence rates for OA (cutoff=36.5 errors) and DA (cutoff=6 errors); PPV was good for DR (cutoff=9 errors). Error scores above cutoffs favored diagnosis of AD. Performance on OA was significantly related to medial frontal gray matter atrophy. OA, together with DR and DA, may facilitate assessment of bvFTD as a novel probe of medial frontal function.

Effects of gadolinium contrast agent administration on automatic brain tissue classification of patients with multiple sclerosis

BACKGROUND AND PURPOSE

The administration of gadolinium contrast agent is a common part of MR imaging examinations in patients with MS. The presence of gadolinium may affect the outcome of automated tissue classification. The purpose of this study was to investigate the effects of the presence of gadolinium on the automatic segmentation in patients with MS by using the synthetic tissue-mapping method.

MATERIALS AND METHODS

A cohort of 20 patients with clinically definite multiple sclerosis were recruited, and the T1 and T2 relaxation times and proton density were simultaneously quantified before and after the administration of gadolinium. Synthetic tissue-mapping was used to measure white matter, gray matter, CSF, brain parenchymal, and intracranial volumes. For comparison, 20 matched controls were measured twice, without gadolinium.

RESULTS

No differences were observed for the control group between the 2 measurements. For the MS group, significant changes were observed pre- and post-gadolinium in intracranial volume (-13 mL, P < .005) and cerebrospinal fluid volume (-16 mL, P < .005) and the remaining, unclassified non-WM/GM/CSF tissue volume within the intracranial volume (+8 mL, P < .05). The changes in the patient group were much smaller than the differences, compared with the controls, which were -129 mL for WM volume, -22 mL for GM volume, +91 mL for CSF volume, 24 mL for the remaining, unclassified non-WM/GM/CSF tissue volume within the intracranial volume, and -126 mL for brain parenchymal volume. No significant differences were observed for linear regression values against age and Expanded Disability Status Scale.

CONCLUSIONS

The administration of gadolinium contrast agent had a significant effect on automatic brain-tissue classification in patients with MS by using synthetic tissue-mapping. The observed differences, however, were much smaller than the group differences between MS and controls.

VL: a further case of erroneous recollection

We report a single-case study of a female patient (VL) who exhibited frequent episodes of erroneous recollections triggered by everyday events. Based on neuropsychological testing, VL was classified as suffering from mild to moderate dementia (MMSE=18) and was given a diagnosis of probable Alzheimer׳s disease. Her memory functions were uniformly impaired but her verbal abilities were generally well preserved. A structural MRI scan showed extensive areas of gray matter atrophy particularly in frontal and medial-temporal (MTL) areas. Results of experimental recognition tests showed that VL had very high false alarm rates on tests using pictures, faces and auditory stimuli, but lower false alarm rates on verbal tests. We provide a speculative account of her erroneous recollections in terms of her MTL and frontal pathology. In outline, we suggest that owing to binding failures in MTL regions, VL׳s recognition processes were forced to rely on earlier than normal stages of analysis. Environmental features on a given recognition trial may have combined with fragments persisting from previous trials resulting in erroneous feelings of familiarity and of recollection that were not discounted or edited out, due to her impaired frontal processes.

Multiscale segmentation of the skull in MR images for MRI-based attenuation correction of combined MR/PET

BACKGROUND AND OBJECTIVE

Combined magnetic resonance/positron emission tomography (MR/PET) is a relatively new, hybrid imaging modality. MR-based attenuation correction often requires segmentation of the bone on MR images. In this study, we present an automatic segmentation method for the skull on MR images for attenuation correction in brain MR/PET applications.

MATERIALS AND METHODS

Our method transforms T1-weighted MR images to the Radon domain and then detects the features of the skull image. In the Radon domain we use a bilateral filter to construct a multiscale image series. For the repeated convolution we increase the spatial smoothing in each scale and make the width of the spatial and range Gaussian function doubled in each scale. Two filters with different kernels along the vertical direction are applied along the scales from the coarse to fine levels. The results from a coarse scale give a mask for the next fine scale and supervise the segmentation in the next fine scale. The use of the multiscale bilateral filtering scheme is to improve the robustness of the method for noise MR images. After combining the two filtered sinograms, the reciprocal binary sinogram of the skull is obtained for the reconstruction of the skull image.

RESULTS

This method has been tested with brain phantom data, simulated brain data, and real MRI data. For real MRI data the Dice overlap ratios are 92.2%±1.9% between our segmentation and manual segmentation.

CONCLUSIONS

The multiscale segmentation method is robust and accurate and can be used for MRI-based attenuation correction in combined MR/PET.

Segmentation of brain MRI using SOM-FCM-based method and 3D statistical descriptors

Current medical imaging systems provide excellent spatial resolution, high tissue contrast, and up to 65535 intensity levels. Thus, image processing techniques which aim to exploit the information contained in the images are necessary for using these images in computer-aided diagnosis (CAD) systems. Image segmentation may be defined as the process of parcelling the image to delimit different neuroanatomical tissues present on the brain. In this paper we propose a segmentation technique using 3D statistical features extracted from the volume image. In addition, the presented method is based on unsupervised vector quantization and fuzzy clustering techniques and does not use any a priori information. The resulting fuzzy segmentation method addresses the problem of partial volume effect (PVE) and has been assessed using real brain images from the Internet Brain Image Repository (IBSR).

Quantified MRI and cognition in TBI with diffuse and focal damage☆

In patients with chronic-phase traumatic brain injury (TBI), structural MRI is readily attainable and provides rich anatomical information, yet the relationship between whole-brain structural MRI measures and neurocognitive outcome is relatively unexplored and can be complicated by the presence of combined focal and diffuse injury. In this study, sixty-three patients spanning the full range of TBI severity received high-resolution structural MRI concurrent with neuropsychological testing. Multivariate statistical analysis assessed covariance patterns between volumes of grey matter, white matter, and sulcal/subdural and ventricular CSF across 38 brain regions and neuropsychological test performance. Patients with diffuse and diffuse + focal injury were analyzed both separately and together. Tests of speeded attention, working memory, and verbal learning and memory robustly covaried with a distributed pattern of volume loss over temporal, ventromedial prefrontal, right parietal regions, and cingulate regions. This pattern was modulated by the presence of large focal lesions, but held even when analyses were restricted to those with diffuse injury. Effects were most consistently observed within grey matter. Relative to regional brain volumetric data, clinically defined injury severity (depth of coma at time of injury) showed only weak relation to neuropsychological outcome. The results showed that neuropsychological test performance in patients with TBI is related to a distributed pattern of volume loss in regions mediating mnemonic and attentional processing. This relationship holds for patients with and without focal lesions, indicating that diffuse injury alone is sufficient to cause significant neuropsychological disability in relation to regional volume loss. Quantified structural brain imaging data provides a highly sensitive index of brain integrity that is related to cognitive functioning in chronic phase TBI.

Occupational attainment as a marker of cognitive reserve in multiple sclerosis

Cognitive dysfunction affects half of MS patients. Although brain atrophy generally yields the most robust MRI correlations with cognition, significant variance in cognition between individual MS patients remains unexplained. Recently, markers of cognitive reserve such as premorbid intelligence have emerged as important predictors of neuropsychological performance in MS. In the present study, we aimed to extend the cognitive reserve construct by examining the potential contribution of occupational attainment to cognitive decline in MS patients. Brain atrophy, estimated premorbid IQ, and occupational attainment were assessed in 72 MS patients. The Minimal Assessment of Cognitive Functioning in MS was used to evaluate indices of information processing speed, memory, and executive function. Results showed that occupational attainment was a significant predictor of information processing speed, memory, and executive function in hierarchical linear regressions after accounting for brain atrophy and premorbid IQ. These data suggest that MS patients with low occupational attainment fare worse cognitively than those with high occupational attainment after controlling for brain atrophy and premorbid IQ. Occupation, like premorbid IQ, therefore may make an independent contribution to cognitive outcome in MS. Information regarding an individual's occupation is easily acquired and may serve as a useful proxy for cognitive reserve in clinical settings.

Cerebral small vessel disease in aging and Alzheimer's disease: a comparative study using MRI and SPECT

BACKGROUND

White matter hyperintensities (WMH) are associated with aging and are prevalent in various brain pathologies. The purpose of the current study was to characterize WMH perfusion in age-matched elderly controls (ECs) and patients with Alzheimer's disease (ADs).

METHODS

Fifty ECs (23 men) and 61 ADs (33 men) underwent magnetic resonance imaging (MRI), 99mTc-ECD single-photon emission computed tomography (SPECT) and cognitive testing. Brain tissue type was classified on T1 weighted images, and WMH were identified on interleaved proton density/T2 weighted images. Co-registered MR images were used to characterize SPECT perfusion patterns.

RESULTS

WMH perfusion was lower than normal appearing white matter (NAWM) perfusion (P < 0.001) in both EC and AD groups. There was no WMH perfusion difference between groups when considering the mean perfusion from all WMH voxels (P > 0.43). However, locations that were likely to be considered WMH tended to have lower perfusion in ADs compared with ECs. Perfusion gradients along watershed white matter regions were significantly different between EC and AD groups (P < 0.05). A relationship was found between the volume of a WMH lesion and its mean perfusion (P < 0.001) in both ECs and ADs.

CONCLUSION

Global WMH were hypoperfused compared with NAWM to the same degree in EC and AD participants, which suggests a common WMH etiology between groups. However, white matter locations that were likely to contain WMH tended to be hypoperfused in ADs compared with healthy aging. This finding is suggestive of AD-specific pathology that reduces the perfusion at anatomic locations susceptible to the formation of WMH through either the neurodegenerative process or AD-related vasculopathy or both.

A short-term scan-rescan reliability test measuring brain tissue and subcortical hyperintensity volumetrics obtained using the lesion explorer structural MRI processing pipeline

Lesion Explorer (LE) is a reliable and comprehensive MRI-derived tissue segmentation and brain region parcellation processing pipeline for obtaining intracranial tissue and subcortical hyperintensity (SH) volumetrics. The processing pipeline segments: gray (GM) and white matter (WM); sulcal (sCSF) and ventricular cerebrospinal fluid (vCSF); periventricular (pvSH) and deep white subcortical hyperintensities (dwSH); and cystic fluid filled lacunar-like infarcts (Lacunar); into 26 regions of interest. A short-term scan-rescan reliability test was performed on 20 healthy volunteers: 10 older (mean = 77.7 years, SD = 11.1) and 10 younger (mean = 29.4 years, SD = 7.1). Each participant was scanned twice with an average interscan interval of 15.4 days (range: 29 min-50 days). Results suggest low technique-related error as indicated by excellent intraclass correlation coefficient (ICC) results, with ICCs above 0.90 (p < 0.05) for GM, WM, and CSF, in all 26 regions of interest (13 per hemisphere). Ventricular and lesion sub-type (pvSH, dwSH, and Lacunar) volumes also showed high scan-rescan reliability (dwSH = 0.9998, pvSH = 0.9998, Lacunar = 0.9859, p < 0.01). As indicated by the results of this short-term scan-rescan study, the LE structural MRI processing pipeline can be applied for longitudinal volumetric analyses with confidence.

An accurate skull stripping method based on simplex meshes and histogram analysis for magnetic resonance images

Skull stripping methods are designed to eliminate the non-brain tissue in magnetic resonance (MR) brain images. Removal of non-brain tissues is a fundamental step in enabling the processing of brain MR images. The aim of this study is to develop an automatic accurate skull stripping method based on deformable models and histogram analysis. A rough-segmentation step is used to find the optimal starting point for the deformation and is based on thresholds and morphological operators. Thresholds are computed using comparisons with an atlas, and modeling by Gaussians. The deformable model is based on a simplex mesh and its deformation is controlled by the image local gray levels and the information obtained on the gray level modeling of the rough-segmentation. Our Simplex Mesh and Histogram Analysis Skull Stripping (SMHASS) method was tested on the following international databases commonly used in scientific articles: BrainWeb, Internet Brain Segmentation Repository (IBSR), and Segmentation Validation Engine (SVE). A comparison was performed against three of the best skull stripping methods previously published: Brain Extraction Tool (BET), Brain Surface Extractor (BSE), and Hybrid Watershed Algorithm (HWA). Performance was measured using the Jaccard index (J) and Dice coefficient (κ). Our method showed the best performance and differences were statistically significant (p<0.05): J=0.904 and κ=0.950 on BrainWeb; J=0.905 and κ=0.950 on IBSR; J=0.946 and κ=0.972 on SVE.

Magnetic Resonance Image Analysis for Brain CAD Systems with Machine Learning

This chapter describes the image analysis for brain Computer-Aided Diagnosis (CAD) systems with machine learning techniques, which could assist radiologists in the detection of such brain diseases as asymptomatic unruptured aneurysms, Alzheimer’s Disease (AD), vascular dementia, and Multiple Sclerosis (MS) by magnetic resonance imaging. Image analysis in CAD systems consists of image enhancement, initial detection, and image feature extraction, including segmentation. In addition, the authors review the classification of true and false positives using machine learning techniques, as well as the evaluation methods and development cycle for CAD systems.

An in vivo MRI Template Set for Morphometry, Tissue Segmentation, and fMRI Localization in Rats

Over the last decade, several papers have focused on the construction of highly detailed mouse high field magnetic resonance image (MRI) templates via non-linear registration to unbiased reference spaces, allowing for a variety of neuroimaging applications such as robust morphometric analyses. However, work in rats has only provided medium field MRI averages based on linear registration to biased spaces with the sole purpose of approximate functional MRI (fMRI) localization. This precludes any morphometric analysis in spite of the need of exploring in detail the neuroanatomical substrates of diseases in a recent advent of rat models. In this paper we present a new in vivo rat T2 MRI template set, comprising average images of both intensity and shape, obtained via non-linear registration. Also, unlike previous rat template sets, we include white and gray matter probabilistic segmentations, expanding its use to those applications demanding prior-based tissue segmentation, e.g., statistical parametric mapping (SPM) voxel-based morphometry. We also provide a preliminary digitalization of latest Paxinos and Watson atlas for anatomical and functional interpretations within the cerebral cortex. We confirmed that, like with previous templates, forepaw and hindpaw fMRI activations can be correctly localized in the expected atlas structure. To exemplify the use of our new MRI template set, were reported the volumes of brain tissues and cortical structures and probed their relationships with ontogenetic development. Other in vivo applications in the near future can be tensor-, deformation-, or voxel-based morphometry, morphological connectivity, and diffusion tensor-based anatomical connectivity. Our template set, freely available through the SPM extension website, could be an important tool for future longitudinal and/or functional extensive preclinical studies.

Whole-brain white matter disruption in semantic and nonfluent variants of primary progressive aphasia

Semantic (svPPA) and nonfluent (nfPPA) variants of primary progressive aphasia are associated with distinct patterns of cortical atrophy and underlying pathology. Little is known, however, about their contrasting spread of white matter disruption and how this relates to grey matter (GM) loss. We undertook a structural MRI study to investigate this relationship. We used diffusion tensor imaging, tract-based spatial statistics, and voxel-based morphometry to examine fractional anisotropy (FA) and directional diffusivities in nine patients with svPPA and nine patients with nfPPA, and compared them to 16 matched controls after accounting for global GM atrophy. Significant differences in topography of white matter changes were found, with more ventral involvement in svPPA patients and more widespread frontal involvement in nfPPA individuals. However, each group had both ventral and dorsal tract changes, and both showed spread of diffusion abnormalities beyond sites of local atrophy. There was a clear dissociation in sensitivity of diffusion tensor imaging measures between groups. SvPPA patients showed widespread changes in FA and radial diffusivity, whereas changes in axial diffusivity were more restricted and proximal to sites of GM atrophy. NfPPA patients showed isolated changes in FA, but widespread axial and radial diffusivity changes. These findings reveal the extent of white matter disruption in these variants of PPA after accounting for GM loss. Further, they suggest that differences in the relative sensitivity of diffusion metrics may reflect differences in the nature of underlying white matter pathology in these two subtypes.

Visual rating versus volumetry to detect frontotemporal dementia

BACKGROUND/AIMS

Automated, volumetrically defined atrophy in the left anterior cingulate (LAC) and anterior temporal regions (LAT) on MRI can be used to distinguish most patients with frontotemporal dementia (FTD) from controls. FTD and Alzheimer's disease (AD) can differ in the degree of anterior temporal atrophy. We explored whether clinicians can visually detect this atrophy pattern and whether they can use it to classify the 2 groups of dementia patients with the same accuracy.

METHODS

Four neurologists rated atrophy in the LAC and LAT regions on MRI slices from 21 FTD, 21 controls, and 14 AD participants. Inter-rater reliability and diagnostic accuracy were assessed.

RESULTS

All 4 raters agreed on the presence of clinically significant atrophy, and their atrophy scoring correlated with the volumes, but without translation into high inter-rater diagnostic agreement.

CONCLUSIONS

Volumetric analyses are difficult to translate into routine clinical practice.

Imaging genetics in multiple sclerosis: a volumetric and diffusion tensor MRI study of APOE ε4

Evidence linking the ε4 allele of APOE to more severe brain MRI abnormalities in multiple sclerosis (MS) has been conflicting and limited to studies of lesion load and whole brain atrophy. The purpose of the present study was to determine whether the ε4 allele of APOE is associated with more extensive brain pathology in MS using structural and diffusion tensor MRI. Using a case-control design, 43 MS patients with the ε4 allele and 47 ε4 negative MS patients underwent structural and diffusion tensor imaging (DTI) at 3T. Hypo- and hyperintense lesion volumes, whole brain and medial temporal volumes, and DTI parameters (fractional anisotropy (FA) and mean diffusivity (MD)) in normal-appearing brain tissue and lesions were compared between the groups. ε4+ and ε4- MS patients were well-matched on demographic characteristics, disease variables, and proportions receiving disease-modifying therapy. ε4+ and ε4- patients did not differ on any MRI or DTI measure. This study refutes a role for the ε4 allele in MRI abnormalities in MS, particularly those linking ε4 to greater T1 hypointense lesion volume and brain atrophy. Previous work on this putative gene-MRI relationship is extended by comparing DTI measures within lesions and normal-appearing brain tissue. A lack of differences in medial temporal regions, areas that have been linked to ε4-associated changes in health and disease, further supports the conclusion that that ε4 is not associated with more subtle MRI markers of brain pathology in MS.

Brain volumetry: an active contour model-based segmentation followed by SVM-based classification

In this paper a novel automatic approach to identify brain structures in magnetic resonance imaging (MRI) is presented for volumetric measurements. The method is based on the idea of active contour models and support vector machine (SVM) classifiers. The main contributions of the presented method are effective modifications on brain images for active contour model and extracting simple and beneficial features for the SVM classifier. The segmentation process starts with a new generation of active contour models, i.e., vector field convolution (VFC) on modified brain images. VFC results are brain images with the least non-brain regions which are passed on to the SVM classification. The SVM features are selected according to the structure of brain tissues, gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). SVM classifiers are trained for each brain tissue based on the set of extracted features. Although selected features are very simple, they are both sufficient and tissue separately effective. Our method validation is done using the gold standard brain MRI data set. Comparison of the results with the existing algorithms is a good indication of our approach's success.

Image segmentation and activity estimation for microPET 11C-raclopride images using an expectation-maximum algorithm with a mixture of Poisson distributions

The objective of this study was to use a mixture of Poisson (MOP) model expectation maximum (EM) algorithm for segmenting microPET images. Simulated rat phantoms with partial volume effect and different noise levels were generated to evaluate the performance of the method. The partial volume correction was performed using an EM deblurring method before the segmentation. The EM-MOP outperforms the EM-MOP in terms of the estimated spatial accuracy, quantitative accuracy, robustness and computing efficiency. To conclude, the proposed EM-MOP method is a reliable and accurate approach for estimating uptake levels and spatial distributions across target tissues in microPET (11)C-raclopride imaging studies.

Diffusion tensor imaging abnormalities in cognitively impaired multiple sclerosis patients

BACKGROUND

Cognitive impairment can add to the burden of disease in patients with multiple sclerosis (MS). The aim of this study was to assess the relative importance of diffusion tensor imaging (DTI) indices derived from normal appearing white matter (NAWM) and grey matter (NAGM) in determining cognitive dysfunction in MS patients.

METHODS

Sixty two MS patients [51 female, mean age = 41 (sd = 9.6) years, median expanded disability status scale (EDSS) = 2.5] meeting modified McDonald criteria for MS underwent neuropsychological testing using the Neuropsychological Screening Battery for MS (NSBMS) and magnetic resonance imaging (MRI, 1.5T GE) that included DTI sequences. Total T1 hypointense and T2 hyperintense lesion volumes were obtained using semi-automated software. Lesion volumes were subtracted from whole-brain parenchyma to obtain measures of NAWM and NAGM. Fractional anisotropy (FA) of NAWM and mean diffusivity (MD) of NAGM were obtained.

RESULTS

Cognitive impairment was present in 11 patients (18%). These patients had higher EDSS scores, were less educated, and were more likely to have secondary progressive MS. They also had higher hypointense (p = 0.001) and hyperintense (p = 0.004) lesion volumes, greater NAWM atrophy (p = 0.007), lower FA of total NAWM (p = 0.003), and higher MD of total NAGM (p = 0.015). Using a logistic regression analysis, and after controlling for demographic and disease-related differences between groups, FA of NAWM emerged as a significant predictor of cognitive impairment adding to the variance derived from lesion and atrophy data.

CONCLUSION

This study underlies the important role of normal-appearing brain tissue in the pathogenesis of MS-related cognitive impairment.

Automatic segmentation of white matter hyperintensities in the elderly using FLAIR images at 3T

Purpose

To determine the precision and accuracy of an automated method for segmenting white matter hyperintensities (WMH) on fast fluid-attenuated inversion-recovery (FLAIR) images in elderly brains at 3T.

Materials and Methods

FLAIR images from 18 individuals (60–82 years, 9 females) with WMH burdens ranging from 1–80 cm³ were used. The protocol included the removal of clearly hyperintense voxels; two-class fuzzy C-means clustering (FCM); and thresholding to segment probable WMH. Two false-positive minimization (FPM) methods using white matter templates were tested. Precision was assessed by adding synthetic hyperintense voxels to brain slices. Accuracy was validated by comparing automatic and manual segmentations. Whole-brain, voxel-wise metrics of similarity, under- and overestimation were used to evaluate both precision and accuracy.

Results

Precision was high, as the lowest accuracy in the synthetic datasets was 93%. Both FPM strategies successfully improved overall accuracy. Whole-brain accuracy for the FCM segmentation alone ranged from 45%–81%, which improved to 75%–85% using the FPM strategies.

Conclusion

The method was accurate across the range of WMH burden typically seen in the elderly. Accuracy levels achieved or exceeded those of other approaches using multispectral and/or more sophisticated pattern recognition methods. J. Magn. Reson. Imaging 2010;31:1311–1322. © 2010 Wiley-Liss, Inc.

Lesion Explorer: a comprehensive segmentation and parcellation package to obtain regional volumetrics for subcortical hyperintensities and intracranial tissue

Subcortical hyperintensities (SH) are a commonly observed phenomenon on MRI of the aging brain (Kertesz et al., 1988). Conflicting behavioral, cognitive and pathological associations reported in the literature underline the need to develop an intracranial volumetric analysis technique to elucidate pathophysiological origins of SH in Alzheimer's disease (AD), vascular cognitive impairment (VCI) and normal aging (De Leeuw et al., 2001; Mayer and Kier, 1991; Pantoni and Garcia, 1997; Sachdev et al., 2008). The challenge is to develop processing tools that effectively and reliably quantify subcortical small vessel disease in the context of brain tissue compartments. Segmentation and brain region parcellation should account for SH subtypes which are often classified as: periventricular (pvSH) and deep white (dwSH), incidental white matter disease or lacunar infarcts and Virchow-Robin spaces. Lesion Explorer (LE) was developed as the final component of a comprehensive volumetric segmentation and parcellation image processing stream built upon previously published methods (Dade et al., 2004; Kovacevic et al., 2002). Inter-rater and inter-method reliability was accomplished both globally and regionally. Volumetric analysis showed high inter-rater reliability both globally (ICC=.99) and regionally (ICC=.98). Pixel-wise spatial congruence was also high (SI=.97). Whole brain pvSH volumes yielded high inter-rater reliability (ICC=.99). Volumetric analysis against an alternative kNN segmentation revealed high inter-method reliability (ICC=.97). Comparison with visual rating scales showed high significant correlations (ARWMC: r=.86; CHIPS: r=.87). The pipeline yields a comprehensive and reliable individualized volumetric profile for subcortical vasculopathy that includes regionalized (26 brain regions) measures for: GM, WM, sCSF, vCSF, lacunar and non-lacunar pvSH and dwSH.

Brain MRI tissue classification based on local Markov random fields

A new method for tissue classification of brain magnetic resonance images (MRI) of the brain is proposed. The method is based on local image models where each models the image content in a subset of the image domain. With this local modeling approach, the assumption that tissue types have the same characteristics over the brain needs not to be evoked. This is important because tissue type characteristics, such as T1 and T2 relaxation times and proton density, vary across the individual brain and the proposed method offers improved protection against intensity non-uniformity artifacts that can hamper automatic tissue classification methods in brain MRI. A framework in which local models for tissue intensities and Markov Random Field (MRF) priors are combined into a global probabilistic image model is introduced. This global model will be an inhomogeneous MRF and it can be solved by standard algorithms such as iterative conditional modes. The division of the whole image domain into local brain regions possibly having different intensity statistics is realized via sub-volume probabilistic atlases. Finally, the parameters for the local intensity models are obtained without supervision by maximizing the weighted likelihood of a certain finite mixture model. For the maximization task, a novel genetic algorithm almost free of initialization dependency is applied. The algorithm is tested on both simulated and real brain MR images. The experiments confirm that the new method offers a useful improvement of the tissue classification accuracy when the basic tissue characteristics vary across the brain and the noise level of the images is reasonable. The method also offers better protection against intensity non-uniformity artifact than the corresponding method based on a global (whole image) modeling scheme.

Diffusion tensor imaging abnormalities in depressed multiple sclerosis patients

Depression is common in patients with multiple sclerosis, but to date no studies have explored diffusion tensor imaging indices associated with mood change. This study aimed to determine cerebral correlates of depression in multiple sclerosis patients using diffusion tensor imaging. Sixty-two subjects with multiple sclerosis were assessed for depression with the Beck Depression Inventory (BDI-II). All subjects underwent magnetic resonance imaging. Whole brain and regional volumes were calculated for lesions (hyper/hypointense) and normal-appearing white and grey matter. Fractional anisotropy and mean diffusivity were calculated for each brain region. Magnetic resonance imaging comparisons were undertaken between depressed (Beck Depression Inventory > or = 19) and non-depressed subjects. Depressed subjects (n = 30) had a higher hypointense lesion volume in the right medial inferior frontal region, a smaller normal-appearing white matter volume in the left superior frontal region, and lower fractional anisotropy and higher mean diffusivity in the left anterior temporal normal-appearing white matter and normal-appearing grey matter regions, respectively. Depressed subjects also had higher mean diffusivity in right inferior frontal hyperintense lesions. Magnetic resonance imaging variables contributed to 43% of the depression variance. We conclude that the presence of more marked diffusion tensor imaging abnormalities in the normal-appearing white matter and normal-appearing grey matter of depressed subjects highlights the importance of more subtle measures of structural brain change in the pathogenesis of depression.