Gray Matter Patterns Research Articles

Neural signatures of second language learning and control

Experience with multiple languages has unique effects on cortical structure and information processing. Differences in gray matter density and patterns of cortical activation are observed in lifelong bilinguals compared to monolinguals as a result of their experience managing interference across languages. Monolinguals who acquire a second language later in life begin to encounter the same type of linguistic interference as bilinguals, but with a different pre-existing language architecture. The current study used functional magnetic resonance imaging to explore the beginning stages of second language acquisition and cross-linguistic interference in monolingual adults. We found that after English monolinguals learned novel Spanish vocabulary, English and Spanish auditory words led to distinct patterns of cortical activation, with greater recruitment of posterior parietal regions in response to English words and of left hippocampus in response to Spanish words. In addition, cross-linguistic interference from English influenced processing of newly-learned Spanish words, decreasing hippocampus activity. Results suggest that monolinguals may rely on different memory systems to process a newly-learned second language, and that the second language system is sensitive to native language interference.

A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets.

In this paper we propose a web-based approach for quick visualization of big data from brain magnetic resonance imaging (MRI) scans using a combination of an automated image capture and processing system, nonlinear embedding, and interactive data visualization tools. We draw upon thousands of MRI scans captured via the COllaborative Imaging and Neuroinformatics Suite (COINS). We then interface the output of several analysis pipelines based on structural and functional data to a t-distributed stochastic neighbor embedding (t-SNE) algorithm which reduces the number of dimensions for each scan in the input data set to two dimensions while preserving the local structure of data sets. Finally, we interactively display the output of this approach via a web-page, based on data driven documents (D3) JavaScript library. Two distinct approaches were used to visualize the data. In the first approach, we computed multiple quality control (QC) values from pre-processed data, which were used as inputs to the t-SNE algorithm. This approach helps in assessing the quality of each data set relative to others. In the second case, computed variables of interest (e.g., brain volume or voxel values from segmented gray matter images) were used as inputs to the t-SNE algorithm. This approach helps in identifying interesting patterns in the data sets. We demonstrate these approaches using multiple examples from over 10,000 data sets including (1) quality control measures calculated from phantom data over time, (2) quality control data from human functional MRI data across various studies, scanners, sites, (3) volumetric and density measures from human structural MRI data across various studies, scanners and sites. Results from (1) and (2) show the potential of our approach to combine t-SNE data reduction with interactive color coding of variables of interest to quickly identify visually unique clusters of data (i.e., data sets with poor QC, clustering of data by site) quickly. Results from (3) demonstrate interesting patterns of gray matter and volume, and evaluate how they map onto variables including scanners, age, and gender. In sum, the proposed approach allows researchers to rapidly identify and extract meaningful information from big data sets. Such tools are becoming increasingly important as datasets grow larger.

Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis.

Purpose To investigate the diagnostic accuracy of an image-based classifier to distinguish between Alzheimer disease (AD) and behavioral variant frontotemporal dementia (bvFTD) in individual patients by using gray matter (GM) density maps computed from standard T1-weighted structural images obtained with multiple imagers and with independent training and prediction data. Materials and Methods The local institutional review board approved the study. Eighty-four patients with AD, 51 patients with bvFTD, and 94 control subjects were divided into independent training (n = 115) and prediction (n = 114) sets with identical diagnosis and imager type distributions. Training of a support vector machine (SVM) classifier used diagnostic status and GM density maps and produced voxelwise discrimination maps. Discriminant function analysis was used to estimate suitability of the extracted weights for single-subject classification in the prediction set. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for image-based classifiers and neuropsychological z scores. Results Training accuracy of the SVM was 85% for patients with AD versus control subjects, 72% for patients with bvFTD versus control subjects, and 79% for patients with AD versus patients with bvFTD (P ≤ .029). Single-subject diagnosis in the prediction set when using the discrimination maps yielded accuracies of 88% for patients with AD versus control subjects, 85% for patients with bvFTD versus control subjects, and 82% for patients with AD versus patients with bvFTD, with a good to excellent AUC (range, 0.81-0.95; P ≤ .001). Machine learning-based categorization of AD versus bvFTD based on GM density maps outperforms classification based on neuropsychological test results. Conclusion The SVM can be used in single-subject discrimination and can help the clinician arrive at a diagnosis. The SVM can be used to distinguish disease-specific GM patterns in patients with AD and those with bvFTD as compared with normal aging by using common T1-weighted structural MR imaging. (©) RSNA, 2015.

Clinically Isolated Syndrome Suggestive of Multiple Sclerosis: Dynamic Patterns of Gray and White Matter Changes-A 2-year MR Imaging Study.

To investigate the patterns of regional gray matter (GM) and white matter (WM) atrophy, WM microstructural tissue damage, and changes in patients with a clinically isolated syndrome (CIS) suggestive of multiple sclerosis at 2 years from clinical onset. Institutional review board approval and written informed consent from all patients were obtained. Neurologic assessment and conventional, diffusion-tensor, and volumetric brain MR imaging sequences were performed in 37 patients with CIS within 2 months of clinical onset, and after 3, 12, and 24 months. Fourteen healthy control subjects also were studied. Longitudinal GM and WM volume changes and WM microstructural abnormalities were assessed by using voxel-based morphometry (P < .001, uncorrected) and tract-based spatial statistics (P < .05, corrected). At 24 months, 33 of 37 (89%) patients had developed multiple sclerosis. At month 3, patients with CIS showed a transient volume increase in frontal, parietal, temporal, and cerebellar GM regions. At 12 months, patients with CIS developed atrophy of the thalami, caudate nuclei, cerebellum, and frontal, parietal, and temporal lobes. At 24 months GM volume of the frontal, temporal, and parietal cortical areas further decreased from that at 12 months. WM atrophy involved only a few WM regions at 2 months from clinical onset, with progressive involvement of additional WM tracts with time. A diffuse pattern of WM microstructural abnormalities was detected within 2 months of onset and had worsened at 24 months. After an acute inflammatory event, dynamic modifications of regional GM and WM damage occur in patients with CIS, with a progressive evolution of WM damage from disease onset and a transient, early increase in GM volume, followed by GM atrophy. Neurodegenerative processes start early in patients with multiple sclerosis.

Sex differences in structural organization of motor systems and their dissociable links with repetitive/restricted behaviors in children with autism.

BackgroundAutism spectrum disorder (ASD) is diagnosed much less often in females than males. Emerging behavioral accounts suggest that the clinical presentation of autism is different in females and males, yet research examining sex differences in core symptoms of autism in affected children has been limited. Additionally, to date, there have been no systematic attempts to characterize neuroanatomical differences underlying the distinct behavioral profiles observed in girls and boys with ASD. This is in part because extant ASD studies have included a small number of girls.MethodsLeveraging the National Database for Autism Research (NDAR), we first analyzed symptom severity in a large sample consisting of 128 ASD girls and 614 age- and IQ-matched ASD boys. We then examined symptom severity and structural imaging data using novel multivariate pattern analysis in a well-matched group of 25 ASD girls, 25 ASD boys, 19 typically developing (TD) girls, and 19 TD boys, obtained from the Autism Brain Imaging Data Exchange (ABIDE).ResultsIn both the NDAR and ABIDE datasets, girls, compared to boys, with ASD showed less severe repetitive/restricted behaviors (RRBs) and comparable deficits in the social and communication domains. In the ABIDE imaging dataset, gray matter (GM) patterns in the motor cortex, supplementary motor area (SMA), cerebellum, fusiform gyrus, and amygdala accurately discriminated girls and boys with ASD. This sex difference pattern was specific to ASD as the GM in these brain regions did not discriminate TD girls and boys. Moreover, GM in the motor cortex, SMA, and crus 1 subdivision of the cerebellum was correlated with RRB in girls whereas GM in the right putamen—the region that discriminated TD girls and boys—was correlated with RRB in boys.ConclusionsWe found robust evidence for reduced levels of RRB in girls, compared to boys, with ASD, providing the strongest evidence to date for sex differences in a core phenotypic feature of childhood ASD. Sex differences in brain morphometry are prominent in the motor system and in areas that comprise the “social brain.” Notably, RRB severity is associated with sex differences in GM morphometry in distinct motor regions. Our findings provide novel insights into the neurobiology of sex differences in childhood autism.Electronic supplementary materialThe online version of this article (doi:10.1186/s13229-015-0042-z) contains supplementary material, which is available to authorized users.

Microstructural changes across different clinical milestones of disease in amyotrophic lateral sclerosis.

Neurodegenerative process in amyotrophic lateral sclerosis (ALS) has been proven to involve several cortical and subcortical brain regions within and beyond motor areas. However, how ALS pathology spreads progressively during disease evolution is still unknown. In this cross-sectional study we investigated 54 ALS patients, divided into 3 subsets according to the clinical stage, and 18 age and sex-matched healthy controls, by using tract-based spatial statistics (TBSS) diffusion tensor imaging (DTI) and voxel-based morphometry (VBM) analyses. We aimed to identify white (WM) and gray matter (GM) patterns of disease distinctive of each clinical stage, corresponding to specific clinical milestones. ALS cases in stage 2A (i.e., at diagnosis) were characterized by GM and WM impairment of left motor and premotor cortices and brainstem at ponto-mesenchephalic junction. ALS patients in clinical stage 2B (with impairment of two functional regions) exhibited decreased fractional anisotropy (FA) (p<0.001, uncorrected) and increased mean (MD) and radial diffusivity (RD) (p<0.001, uncorrected) in the left cerebellar hemisphere and brainstem precerebellar nuclei, as well as in motor areas, while GM atrophy (p<0.001, uncorrected) was detected only in the left inferior frontal gyrus and right cuneus. Finally, ALS patients in stage 3 (with impairment of three functional regions) exhibited decreased FA and increased MD and RD (p<0.05, corrected) within WM underneath bilateral pre and postcentral gyri, corpus callosum midbody, long associative tracts and midbrain, while no significant clusters of GM atrophy were observed. Our findings reinforce the hypothesis that the neurodegenerative process propagates along the axonal pathways and develops beyond motor areas from early stages, involving progressively several frontotemporal regions and their afferents and efferents, while the detection of GM atrophy in earlier stages and its disappearance in later stages may be the result of reactive gliosis.

Regional volumes and spatial volumetric distribution of gray matter in the gender dysphoric brain

The sexual differentiation of the brain is primarily driven by gonadal hormones during fetal development. Leading theories on the etiology of gender dysphoria (GD) involve deviations herein. To examine whether there are signs of a sex-atypical brain development in GD, we quantified regional neural gray matter (GM) volumes in 55 female-to-male and 38 male-to-female adolescents, 44 boys and 52 girls without GD and applied both univariate and multivariate analyses. In girls, more GM volume was observed in the left superior medial frontal cortex, while boys had more volume in the bilateral superior posterior hemispheres of the cerebellum and the hypothalamus. Regarding the GD groups, at whole-brain level they differed only from individuals sharing their gender identity but not from their natal sex. Accordingly, using multivariate pattern recognition analyses, the GD groups could more accurately be automatically discriminated from individuals sharing their gender identity than those sharing their natal sex based on spatially distributed GM patterns. However, region of interest analyses indicated less GM volume in the right cerebellum and more volume in the medial frontal cortex in female-to-males in comparison to girls without GD, while male-to-females had less volume in the bilateral cerebellum and hypothalamus than natal boys. Deviations from the natal sex within sexually dimorphic structures were also observed in the untreated subsamples. Our findings thus indicate that GM distribution and regional volumes in GD adolescents are largely in accordance with their respective natal sex. However, there are subtle deviations from the natal sex in sexually dimorphic structures, which can represent signs of a partial sex-atypical differentiation of the brain.

Hereditary Spastic Paraplegia: Beyond Clinical Phenotypes toward a Unified Pattern of Central Nervous System Damage.

To investigate whether specific patterns of brain gray matter (GM) regional volumes and white matter (WM) microstructural abnormalities and spinal cord atrophy occur in patients with pure and complicated hereditary spastic paraplegias (HSPs). Relationships between clinical and cognitive features of patients with HSP who had brain and cervical cord damage were also investigated. This study was approved by the local ethical committees on human studies, and written informed consent from all subjects was obtained prior to enrollment. Forty-four patients with HSP (20 genetically defined cases and 24 without genetic diagnosis) and 19 healthy control subjects underwent clinical, neuropsychological, and advanced magnetic resonance (MR) imaging evaluations. Patterns of GM atrophy and WM microstructural damage obtained by using structural and diffusion-tensor MR imaging were compared between groups. Cervical cord atrophy was also assessed by using an active surface method. Correlations between clinical, cognitive, and diffusion-tensor MR imaging measures were evaluated. Clinical data showed that spastic paraplegia is accompanied by a number of other features, including sensory disturbances, and verbal and spatial memory deficits, not only in complicated HSP but also in pure HSP. MR imaging demonstrated a similar involvement of motor, association, and cerebellar WM pathways (P < .05, family-wise error corrected for multiple comparisons) and cervical cord (P < .001) in patients with HSP relative to healthy control subjects, regardless of their clinical picture. The severity of WM damage correlated with the degree of spasticity (P < .05, family-wise error corrected) and cognitive impairment (r values, -0.39 to 0.51; P values, .001-.05) in both pure and complicated HSP. The detection of a distributed pattern of central nervous system damage in patients with pure and complicated HSP suggests that the "primary" corticospinal tract involvement known to occur in these patients may be associated with a neurodegenerative process, which spreads out to extramotor regions, likely via anatomic connections. This observation is in line with emerging pieces of evidence that, independent of the clinical phenotype, there is a common neurodegenerative cascade shared by different neurologic disorders.

Diffuse decreased gray matter in patients with idiopathic craniocervical dystonia: a voxel-based morphometry study.

Background: Recent studies have addressed the role of structures other than the basal ganglia in the pathophysiology of craniocervical dystonia (CCD). Neuroimaging studies have attempted to identify structural abnormalities in CCD but a clear pattern of alteration has not been established. We performed whole-brain evaluation using voxel-based morphometry (VBM) to identify patterns of gray matter (GM) changes in CCD.Methods: We compared 27 patients with CCD matched in age and gender to 54 healthy controls. VBM was used to compare GM volumes. We created a two-sample t-test corrected for subjects’ age, and we tested with a level of significance of p < 0.001 and false discovery rate (FDR) correction (p < 0.05).Results: Voxel-based morphometry demonstrated significant reductions of GM using p < 0.001 in the cerebellar vermis IV/V, bilaterally in the superior frontal gyrus, precuneus, anterior cingulate and paracingulate, insular cortex, lingual gyrus, and calcarine fissure; in the left hemisphere in the supplementary motor area, inferior frontal gyrus, inferior parietal gyrus, temporal pole, supramarginal gyrus, rolandic operculum, hippocampus, middle occipital gyrus, cerebellar lobules IV/V, superior, and middle temporal gyri; in the right hemisphere, the middle cingulate and precentral gyrus. Our study did not report any significant result using the FDR correction. We also detected correlations between GM volume and age, disease duration, duration of botulinum toxin treatment, and the Marsden–Fahn dystonia scale scores.Conclusion: We detected large clusters of GM changes chiefly in structures primarily involved in sensorimotor integration, motor planning, visuospatial function, and emotional processing.

Patterns of Gray Matter Abnormalities in Schizophrenia Based on an International Mega-analysis.

Analyses of gray matter concentration (GMC) deficits in patients with schizophrenia (Sz) have identified robust changes throughout the cortex. We assessed the relationships between diagnosis, overall symptom severity, and patterns of gray matter in the largest aggregated structural imaging dataset to date. We performed both source-based morphometry (SBM) and voxel-based morphometry (VBM) analyses on GMC images from 784 Sz and 936 controls (Ct) across 23 scanning sites in Europe and the United States. After correcting for age, gender, site, and diagnosis by site interactions, SBM analyses showed 9 patterns of diagnostic differences. They comprised separate cortical, subcortical, and cerebellar regions. Seven patterns showed greater GMC in Ct than Sz, while 2 (brainstem and cerebellum) showed greater GMC for Sz. The greatest GMC deficit was in a single pattern comprising regions in the superior temporal gyrus, inferior frontal gyrus, and medial frontal cortex, which replicated over analyses of data subsets. VBM analyses identified overall cortical GMC loss and one small cluster of increased GMC in Sz, which overlapped with the SBM brainstem component. We found no significant association between the component loadings and symptom severity in either analysis. This mega-analysis confirms that the commonly found GMC loss in Sz in the anterior temporal lobe, insula, and medial frontal lobe form a single, consistent spatial pattern even in such a diverse dataset. The separation of GMC loss into robust, repeatable spatial patterns across multiple datasets paves the way for the application of these methods to identify subtle genetic and clinical cohort effects.

Spatial patterns of structural brain changes in type 2 diabetic patients and their longitudinal progression with intensive control of blood glucose.

OBJECTIVEUnderstanding the effect of diabetes as well as of alternative treatment strategies on cerebral structure is critical for the development of targeted interventions against accelerated neurodegeneration in type 2 diabetes. We investigated whether diabetes characteristics were associated with spatially specific patterns of brain changes and whether those patterns were affected by intensive versus standard glycemic treatment.RESEARCH DESIGN AND METHODSUsing baseline MRIs of 488 participants with type 2 diabetes from the Action to Control Cardiovascular Risk in Diabetes-Memory in Diabetes (ACCORD-MIND) study, we applied a new voxel-based analysis methodology to identify spatially specific patterns of gray matter and white matter volume loss related to diabetes duration and HbA1c. The longitudinal analysis used 40-month follow-up data to evaluate differences in progression of volume loss between intensive and standard glycemic treatment arms.RESULTSParticipants with longer diabetes duration had significantly lower gray matter volumes, primarily in certain regions in the frontal and temporal lobes. The longitudinal analysis of treatment effects revealed a heterogeneous pattern of decelerated loss of gray matter volume associated with intensive glycemic treatment. Intensive treatment decelerated volume loss, particularly in regions adjacent to those cross-sectionally associated with diabetes duration. No significant relationship between low versus high baseline HbA1c levels and brain changes was found. Finally, regions in which cognitive change was associated with longitudinal volume loss had only small overlap with regions related to diabetes duration and to treatment effects.CONCLUSIONSApplying advanced quantitative image pattern analysis methods on longitudinal MRI data of a large sample of patients with type 2 diabetes, we demonstrate that there are spatially specific patterns of brain changes that vary by diabetes characteristics and that the progression of gray matter volume loss is slowed by intensive glycemic treatment, particularly in regions adjacent to areas affected by diabetes.

Patterns of regional gray matter and white matter atrophy in cortical multiple sclerosis.

We investigated the patterns of regional distribution of focal lesions, white matter (WM) and gray matter (GM) atrophy in patients with cortical (cort) MS in comparison to classical (c) MS patients. Nine cort-MS, nine c-MS and nine age-matched healthy controls (HC) underwent a brain MRI exam, including FLAIR and high-resolution T1-weighted scans. MS patients underwent neurological and neuropsychological assessment. Between-group differences of GM and WM volumes and their correlations with neuropsychological performances were assessed with voxel-based morphometry. FLAIR and T1 lesion probability maps (LPMs) were also obtained. Performance at neuropsychological tests was worse in cort-MS than in c-MS patients. Compared to HC, MS patients had a distributed pattern of GM and WM atrophy. No GM/WM area was more atrophic in c-MS vs cort-MS patients. Compared to c-MS, cort-MS patients experienced GM atrophy of frontal-temporal-parietal areas and cingulate cortex and WM atrophy of the cingulum bundle, bilateral cerebral peduncles, right inferior longitudinal fasciculus and left superior longitudinal fasciculus. FLAIR and T1 LPMs did not differ between c-MS vs cort-MS patients. A higher susceptibility to neurodegenerative processes in key brain regions known to be related to cognitive functions is likely to underlie the clinical manifestations of cort-MS.

Quantitative application of the primary progressive aphasia consensus criteria

To determine how well the consensus criteria could classify subjects with primary progressive aphasia (PPA) using a quantitative speech and language battery that matches the test descriptions provided by the consensus criteria. A total of 105 participants with a neurodegenerative speech and language disorder were prospectively recruited and underwent neurologic, neuropsychological, and speech and language testing and MRI in this case-control study. Twenty-one participants with apraxia of speech without aphasia served as controls. Select tests from the speech and language battery were chosen for application of consensus criteria and cutoffs were employed to determine syndromic classification. Hierarchical cluster analysis was used to examine participants who could not be classified. Of the 84 participants, 58 (69%) could be classified as agrammatic (27%), semantic (7%), or logopenic (35%) variants of PPA. The remaining 31% of participants could not be classified. Of the unclassifiable participants, 2 clusters were identified. The speech and language profile of the first cluster resembled mild logopenic PPA and the second cluster semantic PPA. Gray matter patterns of loss of these 2 clusters of unclassified participants also resembled mild logopenic and semantic variants. Quantitative application of consensus PPA criteria yields the 3 syndromic variants but leaves a large proportion unclassified. Therefore, the current consensus criteria need to be modified in order to improve sensitivity.

Structural brain abnormalities are related to retinal nerve fiber layer thinning and disease duration in neuromyelitis optica spectrum disorders

Background: Although aquaporin-4 (AQP4) is widely expressed in the human brain cortex, lesions are rare in neuromyelitis optica (NMO) spectrum disorders (NMOSD). Recently, however, several studies have demonstrated occult structural brain atrophy in NMO. Objective: This study aims to investigate magnetic resonance imaging (MRI) patterns of gray matter (GM) and white matter (WM) abnormalities in patients with NMOSD and to assess the visual pathway integrity during disease duration correlation of the retinal nerve fiber layer (RNFL) and pericalcarine cortex thickness. Methods: Twenty-one patients with NMOSD and 34 matched healthy controls underwent both high-field MRI (3T) high-resolution T1-weighted and diffusion-tensor MRI. Voxel-based morphometry, cortical analyses (Freesurfer) and diffusion-tensor imaging (DTI) analyses (TBSS-FSL) were used to investigate brain abnormalities. In addition, RNFL measurement by optic-coherence tomography (OCT) was performed. Results: We demonstrate that NMOSD is associated with GM and WM atrophy, encompassing more frequently the motor, sensory and visual pathways, and that the extent of GM atrophy correlates with disease duration. Furthermore, we demonstrate for the first time a correlation between RNFL and pericalcarine cortical thickness, with cortical atrophy evolving over the course of disease. Conclusions: Our findings indicate a role for retrograde and anterograde neurodegeneration in GM atrophy in NMOSD. However, the presence atrophy encompassing almost all lobes suggests that additional pathomechanisms might also be involved.

In vivo signatures of nonfluent/agrammatic primary progressive aphasia caused by FTLD pathology

To identify early cognitive and neuroimaging features of sporadic nonfluent/agrammatic variant of primary progressive aphasia (nfvPPA) caused by frontotemporal lobar degeneration (FTLD) subtypes. We prospectively collected clinical, neuroimaging, and neuropathologic data in 11 patients with sporadic nfvPPA with FTLD-tau (nfvPPA-tau, n = 9) or FTLD-transactive response DNA binding protein pathology of 43 kD type A (nfvPPA-TDP, n = 2). We analyzed patterns of cognitive and gray matter (GM) and white matter (WM) atrophy at presentation in the whole group and in each pathologic subtype separately. We also considered longitudinal clinical data. At first evaluation, regardless of pathologic FTLD subtype, apraxia of speech (AOS) was the most common cognitive feature and atrophy involved the left posterior frontal lobe. Each pathologic subtype showed few distinctive features. At presentation, patients with nfvPPA-tau presented with mild to moderate AOS, mixed dysarthria with prominent hypokinetic features, clear agrammatism, and atrophy in the GM of the left posterior frontal regions and in left frontal WM. While speech and language deficits were prominent early, within 3 years of symptom onset, all patients with nfvPPA-tau developed significant extrapyramidal motor signs. At presentation, patients with nfvPPA-TDP had severe AOS, dysarthria with spastic features, mild agrammatism, and atrophy in left posterior frontal GM only. Selective mutism occurred early, when general neurologic examination only showed mild decrease in finger dexterity in the right hand. Clinical features in sporadic nfvPPA caused by FTLD subtypes relate to neurodegeneration of GM and WM in frontal motor speech and language networks. We propose that early WM atrophy in nfvPPA is suggestive of FTLD-tau pathology while early selective GM loss might be indicative of FTLD-TDP.

Gray matter atrophy pattern in elderly with subjective memory impairment

BackgroundIndividuals with subjective memory impairment (SMI) report worsening of memory without impairment in cognitive tests. Despite normal cognitive performance, they may be at higher risk of cognitive decline compared with individuals without SMI. MethodsWe used a discriminative function (a support vector machine) trained on an independent data set of 226 healthy control subjects and 191 patients with probable Alzheimer's disease (AD) dementia to characterize the baseline gray matter patterns of 24 individuals with SMI and 53 control subjects. We tested for associations of these gray matter patterns with SMI presence, cognitive performance at baseline, and cognitive decline at follow-up. ResultsIndividuals with SMI showed greater similarity to an AD gray matter pattern compared with control subjects without SMI. In addition, episodic memory decline was associated with an AD gray matter pattern in the SMI group. ConclusionsOur results indicate a link between the gray matter atrophy pattern of patients with AD and the presence of SMI. Furthermore, multivariate pattern recognition approaches seem to be a sensitive method for identifying subtle brain changes that correspond to future memory decline in SMI.

Mild cognitive impairment in Parkinson's disease is associated with a distributed pattern of brain white matter damage

This study assesses the patterns of gray matter (GM) and white matter (WM) damage in patients with Parkinson's disease and mild cognitive impairment (PD-MCI) compared with healthy controls and cognitively unimpaired PD patients (PD-Cu). Three-dimensional T1-weighted and diffusion tensor (DT) magnetic resonance imaging (MRI) scans were obtained from 43 PD patients and 33 healthy controls. Cognition was assessed using a neuropsychological battery. Tract-based spatial statistics was applied to compare DT MRI indices between groups on a voxel-by-voxel basis. Voxel-based morphometry was performed to assess GM atrophy. Thirty PD patients were classified as MCI. Compared with healthy controls, PD-Cu and PD-MCI patients did not have GM atrophy. No region of WM damage was found in PD-Cu patients when compared with healthy controls. Relative to healthy controls and PD-Cu patients, PD-MCI patients showed a distributed pattern of WM abnormalities in the anterior and superior corona radiata, genu, and body of the corpus callosum, and anterior inferior fronto-occipital, uncinate, and superior longitudinal fasciculi, bilaterally. Subtle cognitive decline in PD is associated with abnormalities of frontal and interhemispheric WM connections, and not with GM atrophy. DT MRI might contribute to the identification of structural changes in PD-MCI patients prior to the development of dementia.

Implementation and Validation of an Adaptive Template Registration Method for 18F-Flutemetamol Imaging Data

The spatial normalization of PET amyloid imaging data is challenging because different white and gray matter patterns of negative (Aβ-) and positive (Aβ+) uptake could lead to systematic bias if a standard method is used. In this study, we propose the use of an adaptive template registration method to overcome this problem. Data from a phase II study (n = 72) were used to model amyloid deposition with the investigational PET imaging agent (18)F-flutemetamol. Linear regression of voxel intensities on the standardized uptake value ratio (SUVR) in a neocortical composite region for all scans gave an intercept image and a slope image. We devised a method where an adaptive template image spanning the uptake range (the most Aβ- to the most Aβ+ image) can be generated through a linear combination of these 2 images and where the optimal template is selected as part of the registration process. We applied the method to the (18)F-flutemetamol phase II data using a fixed volume of interest atlas to compute SUVRs. Validation was performed in several steps. The PET-only adaptive template registration method and the MR imaging-based method used in statistical parametric mapping were applied to spatially normalize PET and MR scans, respectively. Resulting transformations were applied to coregistered gray matter probability maps, and the quality of the registrations was assessed visually and quantitatively. For comparison of quantification results with an independent patient-space method, FreeSurfer was used to segment each subject's MR scan and the parcellations were applied to the coregistered PET scans. We then correlated SUVRs for a composite neocortical region obtained with both methods. Furthermore, to investigate whether the (18)F-flutemetamol model could be generalized to (11)C-Pittsburgh compound B ((11)C-PIB), we applied the method to Australian Imaging, Biomarkers and Lifestyle (AIBL) (11)C-PIB scans (n = 285) and compared the PET-only neocortical composite score with the corresponding score obtained with a semimanual method that made use of the subject's MR images for the positioning of regions. Spatial normalization was successful on all scans. Visual and quantitative comparison of the new PET-only method with the MR imaging-based method of statistical parametric mapping indicated that performance was similar in the cortical regions although the new PET-only method showed better registration in the cerebellum and pons reference region area. For the (18)F-flutemetamol quantification, there was a strong correlation between the PET-only and FreeSurfer SUVRs (Pearson r = 0.96). We obtained a similar correlation for the AIBL (11)C-PIB data (Pearson r = 0.94). The derived adaptive template registration method allows for robust, accurate, and fully automated quantification of uptake for (18)F-flutemetamol and (11)C-PIB scans without the use of MR imaging data.

Disorder-Specific Predictive Classification of Adolescents with Attention Deficit Hyperactivity Disorder (ADHD) Relative to Autism Using Structural Magnetic Resonance Imaging

ObjectiveAttention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder, but diagnosed by subjective clinical and rating measures. The study’s aim was to apply Gaussian process classification (GPC) to grey matter (GM) volumetric data, to assess whether individual ADHD adolescents can be accurately differentiated from healthy controls based on objective, brain structure measures and whether this is disorder-specific relative to autism spectrum disorder (ASD).MethodTwenty-nine adolescent ADHD boys and 29 age-matched healthy and 19 boys with ASD were scanned. GPC was applied to make disorder-specific predictions of ADHD diagnostic status based on individual brain structure patterns. In addition, voxel-based morphometry (VBM) analysis tested for traditional univariate group level differences in GM.ResultsThe pattern of GM correctly classified 75.9% of patients and 82.8% of controls, achieving an overall classification accuracy of 79.3%. Furthermore, classification was disorder-specific relative to ASD. The discriminating GM patterns showed higher classification weights for ADHD in earlier developing ventrolateral/premotor fronto-temporo-limbic and stronger classification weights for healthy controls in later developing dorsolateral fronto-striato-parieto-cerebellar networks. Several regions were also decreased in GM in ADHD relative to healthy controls in the univariate VBM analysis, suggesting they are GM deficit areas.ConclusionsThe study provides evidence that pattern recognition analysis can provide significant individual diagnostic classification of ADHD patients and healthy controls based on distributed GM patterns with 79.3% accuracy and that this is disorder-specific relative to ASD. Findings are a promising first step towards finding an objective differential diagnostic tool based on brain imaging measures to aid with the subjective clinical diagnosis of ADHD.

Regional covariance patterns of gray matter alterations in Alzheimer's disease and its replicability evaluation.

To identify regional network covariance patterns of gray matter associated with Alzheimer's disease (AD) and to further evaluate its replicability and stability. This study applied a multivariate analytic approach based on scaled subprofile modeling (SSM) to structural magnetic resonance imaging (MRI) data from 19 patients with AD and 19 healthy controls (HC). We further applied the derived covariance patterns to examine the replicability and stability of AD-associated covariance patterns in an independent dataset (13 AD and 14 HC) acquired with a different scanner. The AD-associated covariance patterns identified from SSM combined principal components mainly involved the temporal lobe and parietal lobe. The expression of covariance patterns was significantly higher in AD patients than HC (t(36) = 5.84, P = 5.75E-7) and predicted the AD/HC group membership (84% sensitivity and 90% specificity). In replicability evaluation, the expression of the forward applied covariance patterns was still statistically significant and had acceptable discriminability (69% sensitivity and 71% specificity). AD patients showed regional gray matter alterations in a reliable covariance manner. The results suggest that SSM has utility for characterizing covariant features, and therefore can assist with further understanding covariance patterns of gray matter in AD based on the view of the network.