Luca Ferrarini

Whole brain resting-state analysis reveals decreased functional connectivity in major depression.

Recently, both increases and decreases in resting-state functional connectivity have been found in major depression. However, these studies only assessed functional connectivity within a specific network or between a few regions of interest, while comorbidity and use of medication was not always controlled for. Therefore, the aim of the current study was to investigate whole-brain functional connectivity, unbiased by a priori definition of regions or networks of interest, in medication-free depressive patients without comorbidity. We analyzed resting-state fMRI data of 19 medication-free patients with a recent diagnosis of major depression (within 6 months before inclusion) and no comorbidity, and 19 age- and gender-matched controls. Independent component analysis was employed on the concatenated data sets of all participants. Thirteen functionally relevant networks were identified, describing the entire study sample. Next, individual representations of the networks were created using a dual regression method. Statistical inference was subsequently done on these spatial maps using voxel-wise permutation tests. Abnormal functional connectivity was found within three resting-state networks in depression: (1) decreased bilateral amygdala and left anterior insula connectivity in an affective network, (2) reduced connectivity of the left frontal pole in a network associated with attention and working memory, and (3) decreased bilateral lingual gyrus connectivity within ventromedial visual regions. None of these effects were associated with symptom severity or gray matter density. We found abnormal resting-state functional connectivity not previously associated with major depression, which might relate to abnormal affect regulation and mild cognitive deficits, both associated with the symptomatology of the disorder.

Hierarchical functional modularity in the resting‐state human brain

Functional magnetic resonance imaging (fMRI) studies have shown that anatomically distinct brain regions are functionally connected during the resting state. Basic topological properties in the brain functional connectivity (BFC) map have highlighted the BFCs small‐world topology. Modularity, a more advanced topological property, has been hypothesized to be evolutionary advantageous, contributing to adaptive aspects of anatomical and functional brain connectivity. However, current definitions of modularity for complex networks focus on nonoverlapping clusters, and are seriously limited by disregarding inclusive relationships. Therefore, BFCs modularity has been mainly qualitatively investigated. Here, we introduce a new definition of modularity, based on a recently improved clustering measurement, which overcomes limitations of previous definitions, and apply it to the study of BFC in resting state fMRI of 53 healthy subjects. Results show hierarchical functional modularity in the brain. Hum Brain Mapp, 2009.

Shape differences of the brain ventricles in Alzheimer's disease.

The brain ventricles are surrounded by gray and white matter structures that are often affected in dementia in general and Alzheimers disease (AD) in particular. Any change of volume or shape occurring in these structures must affect the volume and shape of the ventricles. It is well known that ventricular volume is significantly higher in AD patients compared to age-matched healthy subjects. However, the large overlap between the two volume distributions makes the measurement unsuitable as a biomarker of the disease. The purpose of this work was to assess whether local shape differences of the ventricles can be detected when comparing AD patients and controls. In this work, we captured the ventricles shape and shape variations of 29 AD subjects and 25 age-matched controls, using a fully automatic shape modeling technique. By applying permutation tests on every single node of a mesh representation of the shapes, we identified local areas with significant differences. About 22% of an average surface of the ventricles presented significant difference (P < 0.05) ( approximately 14% of the left against approximately 7% of the right side). We found out that in patients with Alzheimer disease, not only the lateral horns were significantly affected, but also the areas adjacent to the anterior corpus callosum, the splenium of the corpus callosum, the amygdala, the thalamus, the tale of the caudate nuclei (especially the left one), and the head of the left caudate nucleus.

Anatomic considerations of cochlear morphology and its implications for insertion trauma in cochlear implant surgery.

Hypothesis: The goal of this study is to analyze the 3-dimensional anatomy of the cochlear spiral and to investigate the consequences of its course to insertion trauma during cochlear implantation. Background: Insertion trauma in cochlear implant surgery is a feared surgical risk, potentially causing neural degeneration and altered performance of the implant. In literature, insertion trauma is reported to occur at specific locations. This has been ascribed to surgical technique and electrode design in relation to the size of the scala tympani. This study investigates whether there is an underlying anatomic substrate serving as a potential source for insertion trauma at these specific locations. Methods: The 3-dimensional path of the cochlear spiral of 8 human temporal bones was determined by segmentation, skeletonization, distance mapping, and wave propagation technique applied on microcomputer tomography images. Potential pressure points along this path were estimated with linear regression. Results: The cochlear lumen shows a noncontinuous spiraling path leading to potential pressure points during cochlear implantation at the basilar membrane in the region of 180 to 225 (12-14 mm) and 725 degrees (22-26 mm) and at the floor of the scala tympani around 0 to 90, 225 to 270, and 405 to 450 degrees. Conclusion: Our data favor the idea that the intrinsic 3-dimensional cochlear morphology contributes to the risk for insertion trauma during cochlear implantation at specific locations.

Ventricular Shape Biomarkers for Alzheimer's disease in Clinical MR images

The aim of this work was to identify ventricular shape‐based biomarkers in MR images to discriminate between patients with Alzheimers disease (AD) and healthy elderly. Clinical MR images were collected for 58 patients and 28 age‐matched healthy controls. After normalizing all the images the ventricular cerebrospinal fluid was semiautomatically extracted for each subject and an innovative technique for fully automatic shape modeling was applied to generate comparable meshes of all ventricles. The search for potential biomarkers was carried out with repeated permutation tests: results highlighted well‐defined areas of the ventricular surface being discriminating features for AD: the left inferior medial temporal horn, the right medial temporal horn (superior and inferior), and the areas close to the left anterior part of the corpus callosum and the head of the right caudate nucleus. The biomarkers were then used as features to build an intelligent machine for AD detection: a Support Vector Machine was trained on AD and healthy subjects and subsequently tested with leave‐1‐out experiments and validation tests on previously unseen cases. The results showed a sensitivity of 76% for AD, with an overall accuracy of 84%, proving that suitable biomarkers for AD can be detected in clinical MR images. Magn Reson Med 59:260–267, 2008.

MMSE scores correlate with local ventricular enlargement in the spectrum from cognitively normal to Alzheimer disease

In this work, we aimed at correlating focal atrophy in periventricular structures with cognitive function, in the spectrum from healthy subjects to severe Alzheimer disease: 28 subjects with normal cognition and 84 patients presenting various degrees of cognitive impairment were included in the study. The cognitive level of each subject was assessed with the Mini-Mental State Examination (MMSE). Atrophy in periventricular structures was inferred by modeling and analyzing local shape variations of brain ventricles: for a given subject, we distinguished between the severity of atrophy, estimated as local enlargement (in mm) of the ventricular surface relative to an average normal subject, and the extent of atrophy, defined as the percentage of the ventricular surface (global or per anatomical region) significantly different from an average control. Linear regression across subjects was performed to evaluate the correlation between atrophy and MMSE score. The severity of atrophy showed good correlation with MMSE score in the left thalamus, the left temporal horn, the left corona radiata, and the right caudate nuclei. The extent of atrophy showed no significant correlations. In conclusion, the MMSE scores correlate with localized depth of atrophy in well-defined periventricular structures.

Morphological Hippocampal Markers for Automated Detection of Alzheimer's Disease and Mild Cognitive Impairment Converters in Magnetic Resonance Images

In this study, we investigated the use of hippocampal shape-based markers for automatic detection of Alzheimers disease (AD) and mild cognitive impairment converters (MCI-c). Three-dimensional T1-weighted magnetic resonance images of 50 AD subjects, 50 age-matched controls, 15 MCI-c, and 15 MCI-non-converters (MCI-nc) were taken. Manual delineations of both hippocampi were obtained from normalized images. Fully automatic shape modeling was used to generate comparable meshes for both structures. Repeated permutation tests, run over a randomly sub-sampled training set (25 controls and 25 ADs), highlighted shape-based markers, mostly located in the CA1 sector, which consistently discriminated ADs and controls. Support vector machines (SVMs) were trained, using markers from either one or both hippocampi, to automatically classify control and AD subjects. Leave-1-out cross-validations over the remaining 25 ADs and 25 controls resulted in an optimal accuracy of 90% (sensitivity 92%), for markers in the left hippocampus. The same morphological markers were used to train SVMs for MCI-c versus MCI-nc classification: markers in the right hippocampus reached an accuracy (and sensitivity) of 80%. Due to the pattern recognition framework, our results statistically represent the expected performances of clinical set-ups, and compare favorably to analyses based on hippocampal volumes.

Shape analysis of subcortical nuclei in Huntington's disease, global versus local atrophy — Results from the TRACK-HD study

Huntingtons disease (HD) is characterized by brain atrophy. Localized atrophy of a specific structure could potentially be a more sensitive biomarker reflecting neuropathologic changes rather than global volume variation. We examined 90 TRACK-HD participants of which 30 were premanifest HD, 30 were manifest HD and 30 were controls. Using FMRIBs Integrated Registration and Segmentation Tool, segmentations were obtained for the pallidum, caudate nucleus, putamen, thalamus, accumbens nucleus, amygdala, and hippocampus and overall volumes were calculated. A point distribution model of each structure was obtained using Growing and Adaptive Meshes. Permutation testing between groups was performed to detect local displacement in shape between groups. In premanifest HD overall volume loss occurred in the putamen, accumbens and caudate nucleus. Overall volume reductions in manifest HD were found in all subcortical structures, except the amygdala, as compared to controls. In premanifest HD shape analysis showed small areas of displacement in the putamen, pallidum, accumbens and caudate nucleus. When the premanifest group was split into two groups according to predicted disease onset, the premanifest HD group close to expected disease onset showed more pronounced displacements in caudate nucleus and putamen compared to premanifest HD far from disease onset or the total premanifest group. Analysis of shape in manifest HD showed widespread shape differences, most prominently in the caudal part of the accumbens nucleus, body of the caudate nucleus, putamen and dorsal part of the pallidum. We conclude that shape analysis provides new insights in localized intrastructural atrophy patterns in HD, but can also potentially serve as specific target areas for disease tracking.

Shape Abnormalities of the Striatum in Alzheimer's Disease

Postmortem studies show pathological changes in the striatum in Alzheimers disease (AD). Here, we examine the surface of the striatum in AD and assess whether changes of the surface are associated with impaired cognitive functioning. The shape of the striatum (n. accumbens, caudate nucleus, and putamen) was compared between 35 AD patients and 35 individuals without cognitive impairment. The striatum was automatically segmented from 3D T1 magnetic resonance images and automatic shape modeling tools (Growing Adaptive Meshes) were applied for morphometrical analysis. Repeated permutation tests were used to identify locations of consistent shape deformities of the striatal surface in AD. Linear regression models, corrected for age, gender, educational level, head size, and total brain parenchymal volume were used to assess the relation between cognitive performance and local surface deformities. In AD patients, differences of shape were observed on the medial head of the caudate nucleus and on the ventral lateral putamen, but not on the accumbens. The head of the caudate nucleus and ventral lateral putamen are characterized by extensive connections with the orbitofrontal and medial temporal cortices. Severity of cognitive impairment was associated with the degree of deformity of the surfaces of the accumbens, rostral medial caudate nucleus, and ventral lateral putamen. These findings provide evidence for the hypothesis that in AD primarily associative and limbic cerebral networks are affected.

Hippocampal atrophy in people with memory deficits: results from the population-based IPREA study.

BACKGROUND Clinical studies have shown that hippocampal atrophy is present before dementia in people with memory deficits and can predict dementia development. The question remains whether this association holds in the general population. This is of interest for the possible use of hippocampal atrophy to screen population for preventive interventions. The aim of this study was to assess hippocampal volume and shape abnormalities in elderly adults with memory deficits in a cross-sectional population-based study. METHODS We included individuals participating in the Italian Project on the Epidemiology of Alzheimer Disease (IPREA) study: 75 cognitively normal individuals (HC), 31 individuals with memory deficits (MEM), and 31 individuals with memory deficits not otherwise specified (MEMnos). Hippocampal volumes and shape were extracted through manual tracing and the growing and adaptive meshes (GAMEs) shape-modeling algorithm. We investigated between-group differences in hippocampal volume and shape, and correlations with memory deficits. RESULTS In MEM participants, hippocampal volumes were significantly smaller than in HC and were mildly associated with worse memory scores. Memory-associated shape changes mapped to the anterior hippocampus. Shape-based analysis detected no significant difference between MEM and HC, while MEMnos showed shape changes in the posterior hippocampus compared with HC and MEM groups. CONCLUSIONS These findings support the discriminant validity of hippocampal volumetry as a biomarker of memory impairment in the general population. The detection of shape changes in MEMnos but not in MEM participants suggests that shape-based biomarkers might lack sensitivity to detect Alzheimers-like pathology in the general population.