A comparison of Voxel compression mapping & longitudinal Voxel-Based morphometry

Clinical motivation: Serial brain imaging can reveal patterns of change over time with important clinical implications for neurodegenerative disease [1]. We investigate the performance of four analysis methods, in terms of a comparison of 20 patients with probable AD to 20 age- and sex-matched controls, characterising differences in change from baseline to later scans

Ten simple rules for reporting voxel-based morphometry studies

Voxel-based morphometry [Ashburner, J. and Friston, K.J., 2000. Voxel-based morphometry—the methods. NeuroImage 11(6 Pt 1), 805–821] is a commonly used tool for studying patterns of brain change in development or disease and neuroanatomical correlates of subject characteristics. In performing a VBM study, many methodological options are available; if the study is to be easily interpretable and repeatable, the processing steps and decisions must be clearly described. Similarly, unusual methods and parameter choices should be justified in order to aid readers in judging the importance of such options or in comparing the work with other studies. This editorial suggests core principles that should be followed and information that should be included when reporting a VBM study in order to make it transparent, replicable and useful

A Longitudinal Study of Closed Head Injury: Neuropsychological Outcome and Structural Analysis using Region of Interest Measurements and Voxel-Based Morphometry

Background: The hippocampus and corpus callosum have been shown to be vulnerable in head injury. Various neuroimaging modalities and quantitative measurement techniques have been employed to investigate pathological changes in these structures. Cognitive and behavioural deficiencies have also been well documented in head injury. Aims: The aim of this research project was to investigate structural changes in the hippocampus and corpus callosum. Two different quantitative methods were used to measure physical changes and neuropsychological assessment was performed to determine cognitive and behavioural deficit. It was also intended to investigate the relationship between structural change and neuropsychology at 1 and 6 months post injury. Method: Forty-seven patients with head injury (ranging from mild to severe) had undergone a battery of neuropsychological tests and an MRI scan at 1 and 6 months post injury. T1-weighted MRI scans were obtained and analysis of hippocampus and corpus callosum was performed using region-of-interest techniques and voxel-based morphometry which also included comparison to 18 healthy volunteers. The patients completed neuropsychological assessment at 1 and 6 months post injury and data obtained was analysed with respect to each assessment and with structural data to determine cognitive decline and correlation with neuroanatomy. Results: Voxel-based morphometry illustrated reduced whole scan signal differences between patients and controls and changes in patients between 1 and 6 months post injury. Reduced grey matter concentration was also found using voxel-based morphometry and segmented images between patients and controls. A number of neuropsychological aspects were related to injury severity and correlations with neuroanatomy were present. Voxel-based morphometry provided a greater number of associations than region-of-interest analysis. No longitudinal changes were found in the hippocampus or corpus callosum using region-of-interest methodology or voxel-based morphometry. Conclusions: Decreased grey matter concentration identified with voxel-based morphometry illustrated that structural deficit was present in the head injured patients and does not change between 1 and 6 months. Voxel-based morphometry appears more sensitive for detecting structural changes after head injury than region- of-interest methods. Although the majority of patients had suffered mild head injury, cognitive and neurobehavioural deficits were evidenced by a substantial number of patients reporting increased anxiety and depression levels. Also, the findings of relationships between reduced grey matter concentration and cognitive test scores are indicative of the effects of diffuse brain damage in the patient group

Cortical neuronal loss and hippocampal sclerosis are not detected by voxel-based morphometry in individual epilepsy surgery patients

Voxel-based morphometry (VBM) has detected differences between brains of groups of patients with epilepsy and controls, but the sensitivity for detecting subtle pathological changes in single subjects has not been established. The aim of the study was to test the sensitivity of VBM using statistical parametric mapping (SPM5) to detect hippocampal sclerosis (HS) and cortical neuronal loss in individual patients. T1-weighted volumetric 1.5 T MR images from 13 patients with HS and laminar cortical neuronal loss were segmented, normalised and smoothed using SPM5. Both modulated and non-modulated analyses were performed. Comparisons of one control subject against the rest (n ¼ 23) were first performed to ascertain the smoothing level with the lowest number of SPM changes in controls. Each patient was then compared against the whole control group. The lowest number of SPM changes in control subjects was found at a smoothing level of 10 mm full width half maximum for modulated and non-modulated data. In the patient group, no SPM abnormalities were found in the affected temporal lobe or hippocampus at this smoothing level. At lower smoothing levels there were numerous SPM findings in controls and patients. VBM did not detect any abnormalities associated with either laminar cortical neuronal loss or HS. This may be due to normalisation and smoothing of images and low statistical power in areas with larger interindividual differences. This suggests that the methodology may currently not be suitable to detect particular occult abnormalities possibly associated with seizure onset zone in individual epilepsy patients with unremarkable standard structural MRI

Neuroimaging Evidence of Major Morpho-Anatomical and Functional Abnormalities in the BTBR T+TF/J Mouse Model of Autism

BTBR T+tf/J (BTBR) mice display prominent behavioural deficits analogous to the defining symptoms of autism, a feature that has prompted a widespread use of the model in preclinical autism research. Because neuro-behavioural traits are described with respect to reference populations, multiple investigators have examined and described the behaviour of BTBR mice against that exhibited by C57BL/6J (B6), a mouse line characterised by high sociability and low self-grooming. In an attempt to probe the translational relevance of this comparison for autism research, we used Magnetic Resonance Imaging (MRI) to map in both strain multiple morpho-anatomical and functional neuroimaging readouts that have been extensively used in patient populations. Diffusion tensor tractography confirmed previous reports of callosal agenesis and lack of hippocampal commissure in BTBR mice, and revealed a concomitant rostro-caudal reorganisation of major cortical white matter bundles. Intact inter-hemispheric tracts were found in the anterior commissure, ventro-medial thalamus, and in a strain-specific white matter formation located above the third ventricle. BTBR also exhibited decreased fronto-cortical, occipital and thalamic gray matter volume and widespread reductions in cortical thickness with respect to control B6 mice. Foci of increased gray matter volume and thickness were observed in the medial prefrontal and insular cortex. Mapping of resting-state brain activity using cerebral blood volume weighted fMRI revealed reduced cortico-thalamic function together with foci of increased activity in the hypothalamus and dorsal hippocampus of BTBR mice. Collectively, our results show pronounced functional and structural abnormalities in the brain of BTBR mice with respect to control B6 mice. The large and widespread white and gray matter abnormalities observed do not appear to be representative of the neuroanatomical alterations typically observed in autistic patients. The presence of reduced fronto-cortical metabolism is of potential translational relevance, as this feature recapitulates previously-reported clinical observations

Wavelet Features for Recognition of First Episode of Schizophrenia from MRI Brain Images

Machine learning methods are increasingly used in various fields of medicine, contributing to early diagnosis and better quality of care. These outputs are particularly desirable in case of neuropsychiatric disorders, such as schizophrenia, due to the inherent potential for creating a new gold standard in the diagnosis and differentiation of particular disorders. This paper presents a scheme for automated classification from magnetic resonance images based on multiresolution representation in the wavelet domain. Implementation of the proposed algorithm, utilizing support vector machines classifier, is introduced and tested on a dataset containing 104 patients with first episode schizophrenia and healthy volunteers. Optimal parameters of different phases of the algorithm are sought and the quality of classification is estimated by robust cross validation techniques. Values of accuracy, sensitivity and specificity over 71% are achieved

Cerebellar Integrity in the Amyotrophic Lateral Sclerosis - Frontotemporal Dementia Continuum

Amyotrophic lateral sclerosis (ALS) and behavioural variant frontotemporal dementia (bvFTD) are multisystem neurodegenerative disorders that manifest overlapping cognitive, neuropsychiatric and motor features. The cerebellum has long been known to be crucial for intact motor function although emerging evidence over the past decade has attributed cognitive and neuropsychiatric processes to this structure. The current study set out i) to establish the integrity of cerebellar subregions in the amyotrophic lateral sclerosis-behavioural variant frontotemporal dementia spectrum (ALS-bvFTD) and ii) determine whether specific cerebellar atrophy regions are associated with cognitive, neuropsychiatric and motor symptoms in the patients. Seventy-eight patients diagnosed with ALS, ALS-bvFTD, behavioural variant frontotemporal dementia (bvFTD), most without C9ORF72 gene abnormalities, and healthy controls were investigated. Participants underwent cognitive, neuropsychiatric and functional evaluation as well as structural imaging using voxel-based morphometry (VBM) to examine the grey matter subregions of the cerebellar lobules, vermis and crus. VBM analyses revealed: i) significant grey matter atrophy in the cerebellum across the whole ALS-bvFTD continuum; ii) atrophy predominantly of the superior cerebellum and crus in bvFTD patients, atrophy of the inferior cerebellum and vermis in ALS patients, while ALS-bvFTD patients had both patterns of atrophy. Post-hoc covariance analyses revealed that cognitive and neuropsychiatric symptoms were particularly associated with atrophy of the crus and superior lobule, while motor symptoms were more associated with atrophy of the inferior lobules. Taken together, these findings indicate an important role of the cerebellum in the ALS-bvFTD disease spectrum, with all three clinical phenotypes demonstrating specific patterns of subregional atrophy that associated with different symptomology

Grey-matter texture abnormalities and reduced hippocampal volume are distinguishing features of schizophrenia

Neurodevelopmental processes are widely believed to underlie schizophrenia. Analysis of brain texture from conventional magnetic resonance imaging (MRI) can detect disturbance in brain cytoarchitecture. We tested the hypothesis that patients with schizophrenia manifest quantitative differences in brain texture that, alongside discrete volumetric changes, may serve as an endophenotypic biomarker. Texture analysis (TA) of grey matter distribution and voxel-based morphometry (VBM) of regional brain volumes were applied to MRI scans of 27 patients with schizophrenia and 24 controls. Texture parameters (uniformity and entropy) were also used as covariates in VBM analyses to test for correspondence with regional brain volume. Linear discriminant analysis tested if texture and volumetric data predicted diagnostic group membership (schizophrenia or control). We found that uniformity and entropy of grey matter differed significantly between individuals with schizophrenia and controls at the fine spatial scale (filter width below 2 mm). Within the schizophrenia group, these texture parameters correlated with volumes of the left hippocampus, right amygdala and cerebellum. The best predictor of diagnostic group membership was the combination of fine texture heterogeneity and left hippocampal size. This study highlights the presence of distributed grey-matter abnormalities in schizophrenia, and their relation to focal structural abnormality of the hippocampus. The conjunction of these features has potential as a neuroimaging endophenotype of schizophrenia