Gamma ventral capsulotomy for treatment of resistant obsessive-compulsive disorder: A structural MRI pilot prospective study

Objective: The purpose of this study was to investigate regional structural abnormalities in the brains of five patients with refractory obsessive-compulsive disorder (OCD) submitted to gamma ventral capsulotomy. Methods: We acquired morphometric magnetic resonance imaging (MRI) data before and after 1 year of radiosurgery using a 1.5-T MRI scanner. Images were spatially normalized and segmented using optimized voxel-based morphometry (VBM) methods. Voxelwise statistical comparisons between pre- and post-surgery MRI scans were performed using a general linear model. Findings in regions predicted a priori to show volumetric changes (orbitofrontal cortex, anterior cingulate gyrus, basal ganglia and thalamus) were reported as significant if surpassing a statistical threshold of p<0.001 (uncorrected for multiple comparisons). Results: We detected a significant regional postoperative increase in gray matter volume in the right inferior frontal gyri (Brodmann area 47, BA47) when comparing all patients pre and postoperatively. Conclusions: Our results support the current theory of frontal-striatal-thalamic-cortical (FSTC) circuitry involvement in OCD pathogenesis. Gamma ventral capsulotomy is associated with neurobiological changes in the inferior orbitofrontal cortex in refractory OCD patients. (C) 2008 Elsevier Ireland Ltd. All rights reserved.

A structural MRI study of motor conversion disorder: evidence of reduction in thalamic volume.

OBJECTIVE: To investigate potential abnormalities in subcortical brain structures in conversion disorder (CD) compared with controls using a region of interest (ROI) approach. METHODS: Fourteen patients with motor CD were compared with 31 healthy controls using high-resolution MRI scans with an ROI approach focusing on the basal ganglia, thalamus and amygdala. Brain volumes were measured using Freesurfer, a validated segmentation algorithm. RESULTS: Significantly smaller left thalamic volumes were found in patients compared with controls when corrected for intracranial volume. These reductions did not vary with handedness, laterality, duration or severity of symptoms. CONCLUSIONS: These differences may reflect a primary disease process in this area or be secondary effects of the disorder, for example, resulting from limb disuse. Larger, longitudinal structural imaging studies will be required to confirm the findings and explore whether they are primary or secondary to CD.

Advanced feature selection methods in multinominal dementia classification from structural MRI data

Recent studies showed that features extracted from brain MRIs can well discriminate Alzheimer’s disease from Mild Cognitive Impairment. This study provides an algorithm that sequentially applies advanced feature selection methods for findings the best subset of features in terms of binary classification accuracy. The classifiers that provided the highest accuracies, have been then used for solving a multi-class problem by the one-versus-one strategy. Although several approaches based on Regions of Interest (ROIs) extraction exist, the prediction power of features has not yet investigated by comparing filter and wrapper techniques. The findings of this work suggest that (i) the IntraCranial Volume (ICV) normalization can lead to overfitting and worst the accuracy prediction of test set and (ii) the combined use of a Random Forest-based filter with a Support Vector Machines-based wrapper, improves accuracy of binary classification.

Towards the computer-aided diagnosis of dementia based on the geometric and network connectivity of structural MRI data

We present an intuitive geometric approach for analysing the structure and fragility of T1-weighted structural MRI scans of human brains. Apart from computing characteristics like the surface area and volume of regions of the brain that consist of highly active voxels, we also employ Network Theory in order to test how close these regions are to breaking apart. This analysis is used in an attempt to automatically classify subjects into three categories: Alzheimer’s disease, mild cognitive impairment and healthy controls, for the CADDementia Challenge.

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.

An LDA and probability-based classifier for the diagnosis of Alzheimer's Disease from structural MRI

In this paper a custom classification algorithm based on linear discriminant analysis and probability-based weights is implemented and applied to the hippocampus measurements of structural magnetic resonance images from healthy subjects and Alzheimer’s Disease sufferers; and then attempts to diagnose them as accurately as possible. The classifier works by classifying each measurement of a hippocampal volume as healthy controlsized or Alzheimer’s Disease-sized, these new features are then weighted and used to classify the subject as a healthy control or suffering from Alzheimer’s Disease. The preliminary results obtained reach an accuracy of 85.8% and this is a similar accuracy to state-of-the-art methods such as a Naive Bayes classifier and a Support Vector Machine. An advantage of the method proposed in this paper over the aforementioned state of the art classifiers is the descriptive ability of the classifications it produces. The descriptive model can be of great help to aid a doctor in the diagnosis of Alzheimer’s Disease, or even further the understand of how Alzheimer’s Disease affects the hippocampus.

A Genetic Algorithm for the selection of structural MRI features for classification of Mild Cognitive Impairment and Alzheimer's Disease

This work investigates the problem of feature selection in neuroimaging features from structural MRI brain images for the classification of subjects as healthy controls, suffering from Mild Cognitive Impairment or Alzheimer’s Disease. A Genetic Algorithm wrapper method for feature selection is adopted in conjunction with a Support Vector Machine classifier. In very large feature sets, feature selection is found to be redundant as the accuracy is often worsened when compared to an Support Vector Machine with no feature selection. However, when just the hippocampal subfields are used, feature selection shows a significant improvement of the classification accuracy. Three-class Support Vector Machines and two-class Support Vector Machines combined with weighted voting are also compared with the former and found more useful. The highest accuracy achieved at classifying the test data was 65.5% using a genetic algorithm for feature selection with a three-class Support Vector Machine classifier.

An MRI-based approach for the measurement of the dorsolateral prefrontal cortex in humans

The dorsolateral prefrontal cortex (DLPFC) has been implicated in the pathophysiology of mental disorders. Previous region-of-interest MRI studies that attempted to delineate this region adopted various landmarks and measurement techniques, with inconsistent results. We developed a new region-of-interest measurement method to obtain morphometric data of this region from structural MRI scans, taking into account knowledge from cytoarchitectonic postmortem studies and the large inter-individual variability of this region. MRI scans of 10 subjects were obtained, and DLPFC tracing was performed in the coronal plane by two independent raters using the semi-automated software Brains2. The intra-class correlation coefficients between two independent raters were 0.94 for the left DLPFC and 0.93 for the right DLPFC. The mean +/- S.D. DLPFC volumes were 9.23 +/- 2.35 ml for the left hemisphere and 8.20 +/- 2.08 ml for the right hemisphere. Our proposed method has high inter-rater reliability and is easy to implement, permitting the standardized measurement of this region for clinical research applications. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

Diffusion spectrum imaging shows the structural basis of functional cerebellar circuits in the human cerebellum in vivo.

BACKGROUND: The cerebellum is a complex structure that can be affected by several congenital and acquired diseases leading to alteration of its function and neuronal circuits. Identifying the structural bases of cerebellar neuronal networks in humans in vivo may provide biomarkers for diagnosis and management of cerebellar diseases. OBJECTIVES: To define the anatomy of intrinsic and extrinsic cerebellar circuits using high-angular resolution diffusion spectrum imaging (DSI). METHODS: We acquired high-resolution structural MRI and DSI of the cerebellum in four healthy female subjects at 3T. DSI tractography based on a streamline algorithm was performed to identify the circuits connecting the cerebellar cortex with the deep cerebellar nuclei, selected brainstem nuclei, and the thalamus. RESULTS: Using in-vivo DSI in humans we were able to demonstrate the structure of the following cerebellar neuronal circuits: (1) connections of the inferior olivary nucleus with the cerebellar cortex, and with the deep cerebellar nuclei (2) connections between the cerebellar cortex and the deep cerebellar nuclei, (3) connections of the deep cerebellar nuclei conveyed in the superior (SCP), middle (MCP) and inferior (ICP) cerebellar peduncles, (4) complex intersections of fibers in the SCP, MCP and ICP, and (5) connections between the deep cerebellar nuclei and the red nucleus and the thalamus. CONCLUSION: For the first time, we show that DSI tractography in humans in vivo is capable of revealing the structural bases of complex cerebellar networks. DSI thus appears to be a promising imaging method for characterizing anatomical disruptions that occur in cerebellar diseases, and for monitoring response to therapeutic interventions.

Automatic quality assessment in structural brain magnetic resonance imaging

MRI has evolved into an important diagnostic technique in medical imaging. However, reliability of the derived diagnosis can be degraded by artifacts, which challenge both radiologists and automatic computer-aided diagnosis. This work proposes a fully-automatic method for measuring image quality of three-dimensional (3D) structural MRI. Quality measures are derived by analyzing the air background of magnitude images and are capable of detecting image degradation from several sources, including bulk motion, residual magnetization from incomplete spoiling, blurring, and ghosting. The method has been validated on 749 3D T(1)-weighted 1.5T and 3T head scans acquired at 36 Alzheimer's Disease Neuroimaging Initiative (ADNI) study sites operating with various software and hardware combinations. Results are compared against qualitative grades assigned by the ADNI quality control center (taken as the reference standard). The derived quality indices are independent of the MRI system used and agree with the reference standard quality ratings with high sensitivity and specificity (>85%). The proposed procedures for quality assessment could be of great value for both research and routine clinical imaging. It could greatly improve workflow through its ability to rule out the need for a repeat scan while the patient is still in the magnet bore.

Electrical source imaging for presurgical focus localization in epilepsy patients with normal MRI.

PURPOSE: Patients with magnetic resonance (MR)-negative focal epilepsy (MRN-E) have less favorable surgical outcomes (between 40% and 70%) compared to those in whom an MRI lesion guides the site of surgical intervention (60-90%). Patients with extratemporal MRN-E have the worst outcome (around 50% chance of seizure freedom). We studied whether electroencephalography (EEG) source imaging (ESI) of interictal epileptic activity can contribute to the identification of the epileptic focus in patients with normal MRI. METHODS: We carried out ESI in 10 operated patients with nonlesional MRI and a postsurgical follow-up of at least 1 year. Five of the 10 patients had extratemporal lobe epilepsy. Evaluation comprised surface and intracranial EEG monitoring of ictal and interictal events, structural MRI, [(18)F]fluorodeoxyglucose positron emission tomography (FDG-PET), ictal and interictal perfusion single photon emission computed tomography (SPECT) scans. Eight of the 10 patients also underwent intracranial monitoring. RESULTS: ESI correctly localized the epileptic focus within the resection margins in 8 of 10 patients, 9 of whom experienced favorable postsurgical outcomes. DISCUSSION: The results highlight the diagnostic value of ESI and encourage broadening its application to patients with MRN-E. If the surface EEG contains fairly localized spikes, ESI contributes to the presurgical decision process.

Relationship Between Regional Brain Volumes and Cognitive Performance in the Healthy Aging: An MRI Study Using Voxel-Based Morphometry

The presence of cognitive impairment is a frequent complaint among elderly individuals in the general population. This study aimed to investigate the relationship between aging-related regional gray matter (rGM) volume changes and cognitive performance in healthy elderly adults. Morphometric magnetic resonance imaging (MRI) measures were acquired in a community-based sample of 170 cognitively-preserved subjects (66 to 75 years). This sample was drawn from the "Sao Paulo Ageing and Health" study, an epidemiological study aimed at investigating the prevalence and risk factors for Alzheimer's disease in a low income region of the city of Sao Paulo. All subjects underwent cognitive testing using a cross-culturally battery validated by the Research Group on Dementia 10/66 as well as the SKT (applied on the day of MRI scanning). Blood genotyping was performed to determine the frequency of the three apolipoprotein E allele variants (APOE epsilon 2/epsilon 3/epsilon 4) in the sample. Voxelwise linear correlation analyses between rGM volumes and cognitive test scores were performed using voxel-based morphometry, including chronological age as covariate. There were significant direct correlations between worse overall cognitive performance and rGM reductions in the right orbitofrontal cortex and parahippocampal gyrus, and also between verbal fluency scores and bilateral parahippocampal gyral volume (p < 0.05, familywise-error corrected for multiple comparisons using small volume correction). When analyses were repeated adding the presence of the APOE epsilon 4 allele as confounding covariate or excluding a minority of APOE epsilon 2 carriers, all findings retained significance. These results indicate that rGM volumes are relevant biomarkers of cognitive deficits in healthy aging individuals, most notably involving temporolimbic regions and the orbitofrontal cortex.

Childhood abuse is associated with structural impairment in the ventrolateral prefrontal cortex and aggressiveness in patients with borderline personality disorder

Volume reduction and functional impairment in areas of the prefrontal cortex (PFC) have been found in borderline personality disorder (BPD), particularly in patients with a history of childhood abuse. These abnormalities may contribute to the expression of emotion dysregulation and aggressiveness. In this study we investigated whether the volume of the PFC is reduced in BPD patients and whether a history of childhood abuse would be associated with greater PFC structural changes. Structural MRI data were obtained from 18 BPD patients and 19 healthy individuals matched for age, sex, handedness, and education and were analyzed using voxel based morphometry. The Child Abuse Scale was used to elicit a past history of abuse; aggression was evaluated using the Buss-Durkee Hostility Inventory (BDHI). The volume of the right ventrolateral PFC (VLPFC) was significantly reduced in BPD subjects with a history of childhood abuse compared to those without this risk factor. Additionally, right VLPFC gray matter volume significantly correlated with the BDHI total score and with BDHI irritability and negativism subscale scores in patients with a history of childhood abuse. Our results suggest that a history of childhood abuse may lead to increased aggression mediated by an impairment of the right VLPFC. © 2013 Elsevier Ireland Ltd.