Pattern Of Brain Changes Research Articles

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

Evidence of brain injury in fetuses of mothers with preterm labor with intact membranes and preterm premature rupture of membranes

Brain injury and poor neurodevelopment have been consistently reported in infants and adults born before term. These changes occur, at least in part, prenatally and are associated with intra-amniotic inflammation. The pattern of brain changes has been partially documented by magnetic resonance imaging but not by neurosonography along with amniotic fluid brain injury biomarkers. This study aimed to evaluate the prenatal features of brain remodeling and injury in fetuses from patients with preterm labor with intact membranes or preterm premature rupture of membranes and to investigate the potential influence of intra-amniotic inflammation as a risk mediator. In this prospective cohort study, fetal brain remodeling and injury were evaluated using neurosonography and amniocentesis in singleton pregnant patients with preterm labor with intact membranes or preterm premature rupture of membranes between 24.0 and 34.0 weeks of gestation, with (n=41) and without (n=54) intra-amniotic inflammation. The controls for neurosonography were outpatient pregnant patients without preterm labor or preterm premature rupture of membranes matched 2:1 by gestational age at ultrasound. Amniotic fluid controls were patients with an amniocentesis performed for indications other than preterm labor or preterm premature rupture of membranes without brain or genetic defects whose amniotic fluid was collected in our biobank for research purposes matched by gestational age at amniocentesis. The group with intra-amniotic inflammation included those with intra-amniotic infection (microbial invasion of the amniotic cavity and intra-amniotic inflammation) and those with sterile inflammation. Microbial invasion of the amniotic cavity was defined as a positive amniotic fluid culture and/or positive 16S ribosomal RNA gene. Inflammation was defined by amniotic fluid interleukin 6 concentrations of >13.4 ng/mL in preterm labor and >1.43 ng/mL in preterm premature rupture of membranes. Neurosonography included the evaluation of brain structure biometric parameters and cortical development. Neuron-specific enolase, protein S100B, and glial fibrillary acidic protein were selected as amniotic fluid brain injury biomarkers. Data were adjusted for cephalic biometrics, fetal growth percentile, fetal sex, noncephalic presentation, and preterm premature rupture of membranes at admission. Fetuses from mothers with preterm labor with intact membranes or preterm premature rupture of membranes showed signs of brain remodeling and injury. First, they had a smaller cerebellum. Thus, in the intra-amniotic inflammation, non-intra-amniotic inflammation, and control groups, the transcerebellar diameter measurements were 32.7 mm (interquartile range, 29.8-37.6), 35.3 mm (interquartile range, 31.2-39.6), and 35.0 mm (interquartile range, 31.3-38.3), respectively (P=.019), and the vermian height measurements were 16.9 mm (interquartile range, 15.5-19.6), 17.2 mm (interquartile range, 16.0-18.9), and 17.1 mm (interquartile range, 15.7-19.0), respectively (P=.041). Second, they presented a lower corpus callosum area (0.72 mm2 [interquartile range, 0.59-0.81], 0.71 mm2 [interquartile range, 0.63-0.82], and 0.78 mm2 [interquartile range, 0.71-0.91], respectively; P=.006). Third, they showed delayed cortical maturation (the Sylvian fissure depth-to-biparietal diameter ratios were 0.14 [interquartile range, 0.12-0.16], 0.14 [interquartile range, 0.13-0.16], and 0.16 [interquartile range, 0.15-0.17], respectively [P<.001], and the right parieto-occipital sulci depth ratios were 0.09 [interquartile range, 0.07-0.12], 0.11 [interquartile range, 0.09-0.14], and 0.11 [interquartile range, 0.09-0.14], respectively [P=.012]). Finally, regarding amniotic fluid brain injury biomarkers, fetuses from mothers with preterm labor with intact membranes or preterm premature rupture of membranes had higher concentrations of neuron-specific enolase (11,804.6 pg/mL [interquartile range, 6213.4-21,098.8], 8397.7 pg/mL [interquartile range, 3682.1-17,398.3], and 2393.7 pg/mL [interquartile range, 1717.1-3209.3], respectively; P<.001), protein S100B (2030.6 pg/mL [interquartile range, 993.0-4883.5], 1070.3 pg/mL [interquartile range, 365.1-1463.2], and 74.8 pg/mL [interquartile range, 44.7-93.7], respectively; P<.001), and glial fibrillary acidic protein (1.01 ng/mL [interquartile range, 0.54-3.88], 0.965 ng/mL [interquartile range, 0.59-2.07], and 0.24 mg/mL [interquartile range, 0.20-0.28], respectively; P=.002). Fetuses with preterm labor with intact membranes or preterm premature rupture of membranes had prenatal signs of brain remodeling and injury at the time of clinical presentation. These changes were more pronounced in fetuses with intra-amniotic inflammation.

Longitudinal Patterns of Brain Changes in a Community Sample in Relation to Aging and Cognitive Status.

Aging and Alzheimer's disease (AD) are characterized by widespread cortical and subcortical atrophy. Though atrophy patterns between aging and AD overlap considerably, regional differences between these two conditions may exist. Few studies, however, have investigated these patterns in large community samples. Elaborate longitudinal changes in brain morphometry in relation to aging and cognitive status in a well-characterized community cohort. Clinical and neuroimaging data were compiled from 72 participants from the Cardiovascular Health Study-Cognition Study, a community cohort of healthy aging and probable AD participants. Two time points were identified for each participant with a mean follow-up time of 5.36 years. MRI post-processing, morphometric measurements, and statistical analyses were performed using FreeSurfer, Version 7.1.1. Cortical volume was significantly decreased in the bilateral superior frontal, bilateral inferior parietal, and left superior parietal regions, among others. Cortical thickness was significantly reduced in the bilateral superior frontal and left inferior parietal regions, among others. Overall gray and white matter volumes and hippocampal subfields also demonstrated significant reductions. Cortical volume atrophy trajectories between cognitively stable and cognitively declined participants were significantly different in the right postcentral region. Observed volume reductions were consistent with previous studies investigating morphometric brain changes. Patterns of brain atrophy between AD and aging may be different in magnitude but exhibit widespread spatial overlap. These findings help characterize patterns of brain atrophy that may reflect the general population. Larger studies may more definitively establish population norms of aging and AD-related neuroimaging changes.

Treatment Enhances Betweenness Centrality of Fronto-Parietal Network in Parkinson's Patients.

Previous studies have demonstrated a close relationship between early Parkinson’s disease and functional network abnormalities. However, the pattern of brain changes in the early stages of Parkinson’s disease has not been confirmed, which has important implications for the study of clinical indicators of Parkinson’s disease. Therefore, we investigated the functional connectivity before and after treatment in patients with early Parkinson’s disease, and further investigated the relationship between some topological properties and clinicopathological indicators. We included resting state-fMRI (rs-fMRI) data from 27 patients with early Parkinson’s disease aged 50–75 years from the Parkinson’s Disease Progression Markers Initiative (PPMI). The results showed that the functional connectivity of 6 networks, cerebellum network (CBN), cingulo_opercular network (CON), default network (DMN), fronto-parietal network (FPN), occipital network (OCC), and sensorimotor network (SMN), was significantly changed. Compared to before treatment, the main functional connections were concentrated in the CBN after treatment. In addition, the coefficients of these nodes have also changed. For betweenness centrality (BC), the FPN showed a significant improvement in treatment (p < 0.001). In conclusion, the alteration of functional networks in early Parkinson’s patients is critical for clarifying the mechanisms of early diagnosis of the disease.

Longitudinal analysis of regional brain changes in anti-NMDAR encephalitis: a case report

BackgroundAnti-NMDA receptor encephalitis is an immune-mediated disorder characterized by antibodies against the GluN1 subunit of the NMDA receptor that is increasingly recognized as a treatable cause of childhood epileptic encephalopathy. In adults, the disorder has been associated with reversible changes in brain volume over the course of treatment and recovery, but in children, little is known about its time course and associated imaging manifestations.Case presentationA previously healthy 20-month-old boy presented with first-time unprovoked seizures, dysautonomia, and dyskinesia. Paraneoplastic workup was negative, but CSF was positive for anti-NMDAR antibodies. The patient’s clinical condition waxed and waned over a 14-month course of treatment with first- and second-line immunotherapies (including steroids, IVIG, rituximab, and cyclophosphamide). Serial brain MRIs scans obtained at 5 time points spanning this same period showed no abnormal signal or enhancement but were remarkable for cycles of reversible regional cortical volume loss. All scans included identical 1-mm resolution 3D T1-weighted sequences obtained on the same 3 T scanner. Using a novel longitudinal processing stream in FreeSurfer6 (Reuter M, et. al, Neuroimage 61:1402–18, 2012) we quantified the rate of change in cortical volume at each vertex (% volume change per month) between consecutive scans and correlated these changes with the time course of the patient’s treatment and clinical response. We found regionally specific changes in cortical volume (up to 7% per month) that preferentially affected the frontal and occipital lobes and paralleled the patient’s clinical course, with clinical decline associated with volume loss and clinical improvement associated with volume gain.ConclusionsOur results suggest that reversible cortical volume loss in anti-NMDA encephalitis has a regional specificity that mirrors many of the clinical symptoms associated with the disorder and tracks the dynamics of disease severity over time. This case illustrates how quantitative morphometric techniques can be applied to clinical imaging data to reveal patterns of brain change that may provide insight into disease pathophysiology. More widespread application of this approach might reveal regional and temporal patterns specific to different types of autoimmune encephalitis, providing a tool for diagnosis and a surrogate marker for monitoring treatment response.

Brain structure in juvenile-onset Huntington disease.

ObjectiveTo assess brain morphometry in a sample of patients with juvenile-onset Huntington disease (JOHD) and several mouse models of Huntington disease (HD) that likely represent the human JOHD phenotype.MethodsDespite sharing the mutation in the Huntingtin gene, adult-onset HD characteristically presents as a hyperkinetic motor disorder, while JOHD typically presents as a hypokinetic motor disease. The University of Iowa Kids-JHD program enrolls individuals 5 to 25 years of age who have already received the clinical diagnosis. A total of 19 children with juvenile HD (JHD) (mean CAG = 72) were studied. Patients with JHD were compared to healthy controls (n = 234) using a cross-sectional study design. Volumetric data from structural MRI was compared between groups. In addition, we used the same procedure to evaluate brain morphology of R6/2, zQ175, HdhQ250 HD mice models.ResultsParticipants with JHD had substantially reduced intracranial volumes. After controlling for the small intracranial volume size, the volumes of subcortical regions (caudate, putamen, globus pallidus, and thalamus) and of cortical white matter were significantly decreased in patients with JHD. However, the cerebellum was proportionately enlarged in the JHD sample. The cerebral cortex was largely unaffected. Likewise, HD mice had a lower volume of striatum and a higher volume of cerebellum, mirroring the human MRI results.ConclusionsThe primary pathology of JOHD extends beyond changes in the striatal volume. Brain morphology in both mice and human patients with JHD shows proportional cerebellar enlargement. This pattern of brain changes may explain the unique picture of hypokinetic motor symptoms in JHD, which is not seen in the hyperkinetic chorea-like phenotype of adult-onset HD.

Disentangling spatio-temporal patterns of brain changes in large-scale brain imaging databases through independent Gaussian process analysis

Introduction The morphological changes affecting the brain over time are related to several biological processes, governed either by healthy aging or by pathological factors. Since these processes are to date largely unknown, we need statistical approaches to automatically identify these latent morphological evolutions through the analysis of structural brain magnetic resonance images (MRIs). Such approaches require to scale to high-dimensional volumetric observations with application to the analysis of large-scale biomedical databases such as UK Biobank. We present a novel spatio-temporal analysis method, aiming at automatically estimating latent spatio-temporal patterns of brain changes from collections of brain MRIs over time. This approach extends standard methods (such as ICA) to encode priors on spatial and temporal properties of the signal measured in brain images. Methods Our method considers the observed data as a matrix factorization of both temporal and spatial sources. The temporal sources are treated as independent Gaussian processes to promote smoothness in time and model a plausible aging evolution. The spatial sources are modeled as Gaussian random fields to encode the spatial continuity of the brain sub-structures. This particular structure allows to factorize the spatial covariance matrix as a Kronecker product over the three spatial dimensions, which greatly simplifies computations and reduces the dimensions of the matrices we work on. The overall model is efficiently optimized through stochastic variational inference. Results We tested our model on synthetic data. We generated statistically independent temporal sources and spatial sources as smooth heatmaps. Then we trained our model so that it disentangles the observed data in two matrices that best fit the generated mixed observations. Fig. 1 below shows on plot A the original temporal sources (in red) and their approximation by the independent Gaussian processes (in blue). Likewise, plot B shows on top the heatmaps manually generated and under them the maps generated by the algorithm. We can observe that the method is able to capture these raw sources from the noisy observations given as target. Conclusion Eventually this method may provide an ideal exploratory tool for analyzing large-scale medical imaging datasets such as the UK Biobank. Indeed, it allows to efficiently scale to both high-dimensional data and large sample sizes, and also identifies hidden spatio-temporal processes in a completely unsupervised manner.

Structural and Functional Brain Changes at Early and Late Stages of Complex Regional Pain Syndrome

Brain plasticity is demonstrated in complex regional pain syndrome (CRPS), although it is unclear how it modulates at different stages of CRPS. The observation that symptoms can progress over time suggests that the pattern of brain changes might also evolve. We measured structural and functional changes as well as sensorimotor integration at the early stage (ES) and late stage (LS) of CRPS. Twelve ES patients, 16 LS patients, and 16 age- and sex-matched controls were recruited. Gray matter (GM) volume was estimated using voxel-based morphometry. Cerebral perfusion was measured using arterial spin labeling, because it provides a measure of resting neural activity. Connectivity to sensorimotor regions was evaluated using blood-oxygen level-dependent images. The ES group showed reduced GM volume and perfusion in areas associated with spatial body perception, somatosensory cortex, and the limbic system, whereas the LS group exhibited increased perfusion in the motor cortex but no changes in GM volume. However, in the LS group, GM volume in areas associated with pain processing was negatively correlated with average pain levels, likely reflecting a response to ongoing pain. Furthermore, connectivity to sensorimotor cortex showed disruptions in regions associated with motor control and planning, implying impairment of higher-order motor control. PerspectiveThis article presents brain changes at ES and LS of CRPS. We found different patterns of brain changes between these 2 stages. Understanding modulation of brain plasticity at different stages of CRPS could help understand the diversity in outcomes and treatment response and hopefully improve treatment planning.

Structural brain development between childhood and adulthood: Convergence across four longitudinal samples

Longitudinal studies including brain measures acquired through magnetic resonance imaging (MRI) have enabled population models of human brain development, crucial for our understanding of typical development as well as neurodevelopmental disorders. Brain development in the first two decades generally involves early cortical grey matter volume (CGMV) increases followed by decreases, and monotonic increases in cerebral white matter volume (CWMV). However, inconsistencies regarding the precise developmental trajectories call into question the comparability of samples. This issue can be addressed by conducting a comprehensive study across multiple datasets from diverse populations. Here, we present replicable models for gross structural brain development between childhood and adulthood (ages 8–30years) by repeating analyses in four separate longitudinal samples (391 participants; 852 scans). In addition, we address how accounting for global measures of cranial/brain size affect these developmental trajectories. First, we found evidence for continued development of both intracranial volume (ICV) and whole brain volume (WBV) through adolescence, albeit following distinct trajectories. Second, our results indicate that CGMV is at its highest in childhood, decreasing steadily through the second decade with deceleration in the third decade, while CWMV increases until mid-to-late adolescence before decelerating. Importantly, we show that accounting for cranial/brain size affects models of regional brain development, particularly with respect to sex differences. Our results increase confidence in our knowledge of the pattern of brain changes during adolescence, reduce concerns about discrepancies across samples, and suggest some best practices for statistical control of cranial volume and brain size in future studies.

Voxel-based morphometry for separation of schizophrenia from other types of psychosis in first episode psychosis.

Schizophrenia is a psychiatric disorder which involves distortions in thought and perception, blunted affect, and behavioural disturbances. The longer psychosis goes unnoticed and untreated, the more severe the repercussions for relapse and recovery. There is some evidence that early intervention services can help, and diagnostic techniques that could contribute to early intervention may offer clinical utility in these situations. The index test being evaluated in this review is the structural magnetic resonance imaging (MRI) analysis technique known as voxel-based morphometry (VBM) that estimates the distribution of grey matter tissue volume across several brain regions. This review is an exploratory examination of the diagnostic 'potential' of VBM for use as an additional tool in the clinical examination of patients with first episode psychosis to establish whether an individual will progress on to developing schizophrenia as opposed to other types of psychosis. To determine whether VBM applied to the brain can be used to differentiate schizophrenia from other types of psychosis in participants who have received a clinical diagnosis of first episode psychosis. In December 2013, we updated a previous search (May 2012) of MEDLINE, EMBASE, and PsycInfo using OvidSP. We included retrospective and prospective studies that consecutively or randomly selected adolescent and adult participants (< 45 years) with a first episode of psychosis; and that evaluated the diagnostic accuracy of VBM for differentiating schizophrenia from other psychoses compared with a clinical diagnosis made by a qualified mental health professional, with or without the use of standard operational criteria or symptom checklists. We excluded studies in children, and in adult participants with organic brain disorders or who were at high risk for schizophrenia, such as people with a genetic predisposition. Two review authors screened all references for inclusion. We assessed the quality of studies using the QUADAS-2 instrument. Due to a lack of data, we were not able to extract 2 x 2 data tables for each study nor undertake any meta-analysis. We included four studies with a total of 275 participants with first episode psychosis. VBM was not used to diagnose schizophrenia in any of the studies, instead VBM was used to quantify the magnitude of differences in grey matter volume. Therefore, none of the included studies reported data that could be used in the analysis, and we summarised the findings narratively for each study. There is no evidence to currently support diagnosing schizophrenia (as opposed to other psychotic disorders) using the pattern of brain changes seen in VBM studies in patients with first episode psychosis. VBM has the potential to discriminate between diagnostic categories but the methods to do this reliably are currently in evolution. In addition, the lack of applicability of the use of VBM to clinical practice in the studies to date limits the usefulness of VBM as a diagnostic aid to differentiate schizophrenia from other types of psychotic presentations in people with first episode of psychosis.

Brain Structural Effects of Antipsychotic Treatment in Schizophrenia: A Systematic Review.

The findings about the progressive brain changes in schizophrenia are controversial, and the potential confounding effect of antipsychotics on brain structure is still under debate. The goal of the current article was to review the existing longitudinal neuroimaging studies addressing the impact of antipsychotic drug treatment on brain changes in schizophrenia. A comprehensive search of PubMed was performed using combinations of key terms distributed into four blocks: “MRI”, “longitudinal”, “schizophrenia” and “antipsychotic”. Studies were considered to be eligible for the review if they were original articles. Studies that examined only changes in brain density were excluded. A total of 41 MRI studies were identified and reviewed. Longitudinal MRI studies did not provide a consistent notion of the effects of antipsychotic treatment on the pattern of brain changes over time in schizophrenia. Overall, most of the included articles did not find a linear relationship between the degree of exposure and progressive brain changes. Further short- and longterm studies are warranted to a better understanding of the influence of antipsychotics in brain structural changes in schizophrenia and also to verify whether first and second generation antipsychotics may differentially affect brain morphometry.

Possibilities of modern imaging technologies in early diagnosis of Alzheimer disease

Recent advances in neuroimaging technology and image analysis algorithms have significantly contributed to a better understanding of spatial and temporal aspects of brain change associated with Alzheimer Disease. The current review will demonstrate how functional (fMRI) and structural magnetic resonance imaging (MRI) techniques may be used to identify distinct patterns of brain change associated with disease progression and also increased risk for Alzheimer Disease. Moreover, Positron Emission Tomography (PET) based measures of glucosemetabolism (Fluorodeoxyglucose, FDG) and Amyloid-beta plaque density (11-C-Pittsburgh Compound B, PiB and 18-F) will be reviewed regarding their diagnostic value for assessing the individual degree of Alzheimer -pathology and thus complement the information provided by MRI and other clinical measures.

Gender effects on brain changes in early-onset psychosis.

Progressive loss of cortical gray matter (GM) and increase of cerebrospinal fluid (CSF) have been reported in early-onset psychosis (EOP). EOP typically begins during adolescence, a time when developmental brain trajectories differ by gender. This study aimed to determine gender differences in progression of brain changes in this population. A sample of 61 (21 females) adolescents with a first psychotic episode and a matched sample of 70 (23 females) controls underwent both baseline and 2-year follow-up anatomical brain imaging assessments. Regional GM and CSF volumes were obtained using automated methods based on the Talairach's proportional grid system. At baseline, only male patients showed a clear pattern of alterations in the frontal lobe relative to controls (smaller GM and larger CSF volumes). However, parallel longitudinal changes for male and female patients relative to controls were observed, resulting in a common pattern of brain changes across both genders: rate of left frontal lobe GM volume loss was larger in male (-3.8%) and female patients (-4.2%) than in controls (-0.7% males; -0.4% females). The reverse was found for the CSF volume in the left frontal lobe. While the GM and CSF volumes of females with EOP appear to be within the normal range at initial illness onset, our results point to a similar trajectory of increased/accelerated brain changes in both male and female patients with EOP. The pattern of progression of brain changes in psychosis appears to be independent of gender or structural alterations on appearance of psychotic symptoms.

Framingham Coronary Heart Disease Risk Score Can be Predicted from Structural Brain Images in Elderly Subjects.

Recent literature has presented evidence that cardiovascular risk factors (CVRF) play an important role on cognitive performance in elderly individuals, both those who are asymptomatic and those who suffer from symptoms of neurodegenerative disorders. Findings from studies applying neuroimaging methods have increasingly reinforced such notion. Studies addressing the impact of CVRF on brain anatomy changes have gained increasing importance, as recent papers have reported gray matter loss predominantly in regions traditionally affected in Alzheimer’s disease (AD) and vascular dementia in the presence of a high degree of cardiovascular risk. In the present paper, we explore the association between CVRF and brain changes using pattern recognition techniques applied to structural MRI and the Framingham score (a composite measure of cardiovascular risk largely used in epidemiological studies) in a sample of healthy elderly individuals. We aim to answer the following questions: is it possible to decode (i.e., to learn information regarding cardiovascular risk from structural brain images) enabling individual predictions? Among clinical measures comprising the Framingham score, are there particular risk factors that stand as more predictable from patterns of brain changes? Our main findings are threefold: (i) we verified that structural changes in spatially distributed patterns in the brain enable statistically significant prediction of Framingham scores. This result is still significant when controlling for the presence of the APOE 4 allele (an important genetic risk factor for both AD and cardiovascular disease). (ii) When considering each risk factor singly, we found different levels of correlation between real and predicted factors; however, single factors were not significantly predictable from brain images when considering APOE4 allele presence as covariate. (iii) We found important gender differences, and the possible causes of that finding are discussed.

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.

Differential Diagnosis Tool for Parkinsonian Syndrome Using Multiple Structural Brain Measures

Clinical differentiation of parkinsonian syndromes such as the Parkinson variant of multiple system atrophy (MSA-P) and cerebellar subtype (MSA-C) from Parkinson's disease is difficult in the early stage of the disease. To identify the correlative pattern of brain changes for differentiating parkinsonian syndromes, we applied discriminant analysis techniques by magnetic resonance imaging (MRI). T1-weighted volume data and diffusion tensor images were obtained by MRI in eighteen patients with MSA-C, 12 patients with MSA-P, 21 patients with Parkinson's disease, and 21 healthy controls. They were evaluated using voxel-based morphometry and tract-based spatial statistics, respectively. Discriminant functions derived by step wise methods resulted in correct classification rates of 0.89. When differentiating these diseases with the use of three independent variables together, the correct classification rate was the same as that obtained with step wise methods. These findings support the view that each parkinsonian syndrome has structural deviations in multiple brain areas and that a combination of structural brain measures can help to distinguish parkinsonian syndromes.

Mathematical Methods in Biomedical Imaging

Mathematical Methods in Biomedical Imaging

Discrimination of female schizophrenia patients from healthy women using multiple structural brain measures obtained with voxel‐based morphometry

Although schizophrenia and control subjects differ on a variety of neuroanatomical measures, the specificity and sensitivity of any one measure for differentiating between the two groups are low. To identify the correlative pattern of brain changes that best discriminate schizophrenia patients from healthy subjects, discriminant analysis techniques using voxel-based morphometry were applied. The first analysis was conducted to obtain a statistical model that classified 105 female healthy subjects and 38 female schizophrenia patients. First, the differences in gray matter and cerebrospinal fluid volume between the patients and healthy subjects were evaluated using optimized voxel-based morphometry. Then, a discriminant analysis reflecting the results of this evaluation was adopted. The second analysis was performed to prospectively validate the statistical model by successfully classifying a new group that consisted of 23 female healthy subjects and 23 female schizophrenia patients. The use of these variables resulted in correct classification rates of 0.72 in the control subjects and 0.76 in the schizophrenia patients. In the second validation analysis using these variables, correct classification rates of 0.70 in the control subjects and 0.74 in the schizophrenia patients were achieved. Schizophrenia patients have structural deviations in multiple brain areas, and a combination of structural brain measures can distinguish between patients and controls.

Progressive loss of cortical gray matter in schizophrenia: a meta-analysis and meta-regression of longitudinal MRI studies

Cortical gray matter deficits have been found in patients with schizophrenia, with evidence of progression over time. The aim of this study was to determine the extent of progressive cortical gray matter volume changes over time in schizophrenia, their site and time of occurrence, and the role of potential moderators of brain changes. English language articles published between 1 January 1983 and 31 March 2012 in the MEDLINE and EMBASE databases were searched. Longitudinal magnetic resonance imaging studies comparing changes in cortical gray matter volume over time between patients with schizophrenia and healthy controls were included. Hedges g was calculated for each study. Analyses were performed using fixed- and random-effects models. A subgroup analysis was run to explore the pattern of brain changes in patients with first-episode schizophrenia. A meta-regression statistic was adopted to investigate the role of potential moderators of the effect sizes (ESs). A total of 19 studies, analyzing 813 patients with schizophrenia and 718 healthy controls, were included. Over time, patients with schizophrenia showed a significantly higher volume loss of total cortical gray matter, left superior temporal gyrus (STG), left anterior STG, left Heschl gyrus, left planum temporale and posterior STG bilaterally. Meta-analysis of first-episode schizophrenic patients showed a more significant pattern of progressive loss of whole cerebral gray matter volume involving the frontal, temporal and parietal lobes, and left Heschl gyrus compared with healthy controls. Clinical, pharmacologic and neuroradiological variables were found to be significant moderators of brain volume changes in patients with schizophrenia. The meta-analysis demonstrates that progressive cortical gray matter changes in schizophrenia occur with regional and temporal specificity. The underlying pathological process appears to be especially active in the first stages of the disease, affects the left hemisphere and the superior temporal structures more and is at least partly moderated by the type of pharmacological treatment received.

Correlation of Alzheimer disease neuropathologic changes with cognitive status: a review of the literature.

Clinicopathologic correlation studies are critically important for the field of Alzheimer disease (AD) research. Studies on human subjects with autopsy confirmation entail numerous potential biases that affect both their general applicability and the validity of the correlations. Many sources of data variability can weaken the apparent correlation between cognitive status and AD neuropathologic changes. Indeed, most persons in advanced old age have significant non-AD brain lesions that may alter cognition independently of AD. Worldwide research efforts have evaluated thousands of human subjects to assess the causes of cognitive impairment in the elderly, and these studies have been interpreted in different ways. We review the literature focusing on the correlation of AD neuropathologic changes (i.e. β-amyloid plaques and neurofibrillary tangles) with cognitive impairment. We discuss the various patterns of brain changes that have been observed in elderly individuals to provide a perspective for understanding AD clinicopathologic correlation and conclude that evidence from many independent research centers strongly supports the existence of a specific disease, as defined by the presence of Aβ plaques and neurofibrillary tangles. Although Aβ plaques may play a key role in AD pathogenesis, the severity of cognitive impairment correlates best with the burden of neocortical neurofibrillary tangles.