Salvatore Nigro

Surface-based morphometry reveals the neuroanatomical basis of the five-factor model of personality

Abstract The five-factor model (FFM) is a widely used taxonomy of human personality; yet its neuro anatomical basis remains unclear. This is partly because past associations between gray-matter volume and FFM were driven by different surface-based morphometry (SBM) indices (i.e. cortical thickness, surface area, cortical folding or any combination of them). To overcome this limitation, we used Free-Surfer to study how variability in SBM measures was related to the FFM in n = 507 participants from the Human Connectome Project. Neuroticism was associated with thicker cortex and smaller area and folding in prefrontal–temporal regions. Extraversion was linked to thicker pre-cuneus and smaller superior temporal cortex area. Openness was linked to thinner cortex and greater area and folding in prefrontal–parietal regions. Agreeableness was correlated to thinner prefrontal cortex and smaller fusiform gyrus area. Conscientiousness was associated with thicker cortex and smaller area and folding in prefrontal regions. These findings demonstrate that anatomical variability in prefrontal cortices is linked to individual differences in the socio-cognitive dispositions described by the FFM. Cortical thickness and surface area/folding were inversely related each others as a function of different FFM traits (neuroticism, extraversion and consciousness vs openness), which may reflect brain maturational effects that predispose or protect against psychiatric disorders.

Characterizing structural neural networks in de novo Parkinson disease patients using diffusion tensor imaging.

Parkinson disease (PD) can be considered as a brain multisystemic disease arising from dysfunction in several neural networks. The principal aim of this study was to assess whether large‐scale structural topological network changes are detectable in PD patients who have not been exposed yet to dopaminergic therapy (de novo patients). Twenty‐one drug‐naïve PD patients and thirty healthy controls underwent a 3T structural MRI. Next, Diffusion Tensor Imaging (DTI) and graph theoretic analyses to compute individual structural white‐matter (WM) networks were combined. Centrality (degree, eigenvector centrality), segregation (clustering coefficient), and integration measures (efficiency, path length) were assessed in subject‐specific structural networks. Moreover, Network‐based statistic (NBS) was used to identify whether and which subnetworks were significantly different between PD and control participants. De novo PD patients showed decreased clustering coefficient and strength in specific brain regions such as putamen, pallidum, amygdala, and olfactory cortex compared with healthy controls. Moreover, NBS analyses demonstrated that two specific subnetworks of reduced connectivity characterized the WM structural organization of PD patients. In particular, several key pathways in the limbic system, basal ganglia, and sensorimotor circuits showed reduced patterns of communications when comparing PD patients to controls. This study shows that PD is characterized by a disruption in the structural connectivity of several motor and non‐motor regions. These findings provide support to the presence of disconnectivity mechanisms in motor (basal ganglia) as well as in non‐motor (e.g., limbic, olfactory) circuits at an early disease stage of PD. Hum Brain Mapp 37:4500–4510, 2016.

Cortical volume and folding abnormalities in Parkinson's disease patients with pathological gambling

PURPOSE Pathological gambling (PG) is one of the most devastating non-motor complications of Parkinsons disease (PD). Neuroanatomical abnormalities in PD patients with PG are poorly understood. METHODS In the current study we investigated PD patients with and without PG using Voxel Based Morphometry (VBM) and local Gyrification Index (lGI), two neuroimaging techniques useful for detecting complementary morphological metrics in the brain. Twelve PD patients with PG were compared to 12 clinically-matched PD patients without PG and 24 healthy controls. RESULTS PD patients with PG showed grey matter volume loss specifically in the orbitofrontal cortex (OFC) when compared to patients without PG, with the atrophy of this region correlating with the increase of gambling symptoms (G-SAS). Surface-based analysis complemented this evidence revealing that the OFC in the PD patients with PG was also characterized by a reduced lGI. Moreover, when compared to controls, PD patients with PG showed a more widespread anatomical neurodegeneration involving several limbic regions such as: the OFC, cingulate cortex, inferior frontal cortex and insular cortex. Otherwise, demographically-/clinically-matched PD patients without PG did not display significant anatomical changes. DISCUSSION Our study demonstrates that combined grey matter atrophy and reduced lGI in the OFC differentiates PD patients with PG from those without PG, suggesting that this cortical area may play a critical role in the development of this drug-induced behavioral disorder.

Structural 'connectomic' alterations in the limbic system of multiple sclerosis patients with major depression

Background: Major depression (MD) is a common psychiatric disorder in multiple sclerosis (MS). Despite the negative impact of MD on the quality of life of MS patients, little is known about its underlying brain mechanisms. Objective: We studied the whole-brain connectivity patterns that were associated with MD in MS. Alterations were mainly expected within limbic circuits. Methods: Diffusion tensor imaging data were collected in 20 MS patients with MD, 22 non-depressed MS patients and 16 healthy controls. We used deterministic tractography and graph analysis to study the white-matter connectivity patterns that characterized MS patients with MD. Results: We found that MD in MS was associated with increased local path length in the right hippocampus and right amygdala. Further analyses revealed that these effects were driven by an increased shortest distance between both the right hippocampus and right amygdala and a series of regions including the dorsolateral and ventrolateral prefrontal cortex, orbitofrontal cortex, sensory-motor cortices and supplementary motor area. Conclusion: Our data provide strong support for neurobiological accounts positing that MD in MS is mediated by abnormal ‘communications’ within limbic circuits. We also found evidence that MD in MS may be linked with connectivity alterations at the limbic-motor interface, a group of regions that translates emotions into survival-oriented behaviors.

Effect of aging on magnetic resonance measures differentiating progressive supranuclear palsy from Parkinson's disease

Imaging measurements, such as the ratio of the midsagittal areas of the midbrain and pons (midbrain/pons) and the Magnetic Resonance Parkinsonism Index (MRPI), have been proposed to differentiate progressive supranuclear palsy (PSP) from Parkinsons disease (PD). However, abnormal midbrain/pons values suggestive of PSP have also been reported in elderly individuals and in patients with PD. We investigated the effect of aging on single or combined imaging measurements of the brainstem. We calculated the midbrain/pons and the MRPI (the ratio of the midsagittal areas of the pons and the midbrain multiplied by the ratio of the middle cerebellar peduncle and superior cerebellar peduncle widths) in 152 patients affected by PD, 25 patients with PSP, and a group of 81 age‐matched and sex‐matched healthy controls using a 3‐Tesla magnetic resonance imaging scanner. In healthy controls, aging was negatively correlated with midsagittal area of the midbrain and midbrain/pons values. In patients with PD, in addition to the effect of aging, the disease status further influenced the midbrain/pons values (R2 = 0.23; P < 0.001). In both groups, MRPI values were not influenced either by aging or by disease status. No effect of aging on either midbrain/pons or MRPI values was shown in the patients with PSP. Our findings indicated that the MRPI was not significantly influenced by aging or disease‐related changes occurring in PD; whereas, in contrast, the midbrain/pons was influenced. Therefore, the MRPI appears to be a more reliable imaging measurement compared with midbrain/pons values for differentiating PSP from PD and controls in an elderly population.

Hippocampal Subfield Atrophies in Converted and Not-Converted Mild Cognitive Impairments Patients by a Markov Random Fields Algorithm

Although measurement of total hippocampal volume is considered as an important hallmark of Alzheimers disease (AD), recent evidence demonstrated that atrophies of hippocampal subregions might be more sensitive in predicting this neurodegenerative disease. The vast majority of neuroimaging papers investigating this topic are focused on the difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will or not convert in AD. For this reason, the aim of this study was to determine if measurements of hippocampal subfields provide advantages over total hippocampal volume for discriminating these groups. Hippocampal subfields volumetry was extracted in 55 AD, 32 converted and 89 not-converted MCI (c/nc-MCI) and 47 healthy controls, using an atlas-based automatic algorithm based on Markov random fields embedded in the Freesurfer framework. To evaluate the impact of hippocampal atrophy in discriminating the insurgence of AD-like phenotypes we used three classification methods: Support Vector Machine, Naïve Bayesian Classifier and Neural Networks Classifier. Taking into account only the total hippocampal volume, all classification models, reached a sensitivity of about 66% in discriminating between c-MCI and nc-MCI. Otherwise, classification analysis considering all segmenting subfields increased accuracy to diagnose c-MCI from 68% to 72%. This effect resulted to be strongly dependent upon atrophies of the subiculum and presubiculum. Our multivariate analysis revealed that the magnitude of the difference considering hippocampal subfield volumetry, as segmented by the considered atlas-based automatic algorithm, offers an advantage over hippocampal volume in distinguishing early AD from nc-MCI.

Magnetic resonance support vector machine discriminates essential tremor with rest tremor from tremor-dominant Parkinson disease

The aim of the current study was to distinguish patients who had tremor‐dominant Parkinsons disease (tPD) from those who had essential tremor with rest tremor (rET).

Neuroticism modulates brain visuo-vestibular and anxiety systems during a virtual rollercoaster task

Different lines of research suggest that anxiety‐related personality traits may influence the visual and vestibular control of balance, although the brain mechanisms underlying this effect remain unclear. To our knowledge, this is the first functional magnetic resonance imaging (fMRI) study that investigates how individual differences in neuroticism and introversion, two key personality traits linked to anxiety, modulate brain regional responses and functional connectivity patterns during a fMRI task simulating self‐motion. Twenty‐four healthy individuals with variable levels of neuroticism and introversion underwent fMRI while performing a virtual reality rollercoaster task that included two main types of trials: (1) trials simulating downward or upward self‐motion (vertical motion), and (2) trials simulating self‐motion in horizontal planes (horizontal motion). Regional brain activity and functional connectivity patterns when comparing vertical versus horizontal motion trials were correlated with personality traits of the Five Factor Model (i.e., neuroticism, extraversion‐introversion, openness, agreeableness, and conscientiousness). When comparing vertical to horizontal motion trials, we found a positive correlation between neuroticism scores and regional activity in the left parieto‐insular vestibular cortex (PIVC). For the same contrast, increased functional connectivity between the left PIVC and right amygdala was also detected as a function of higher neuroticism scores. Together, these findings provide new evidence that individual differences in personality traits linked to anxiety are significantly associated with changes in the activity and functional connectivity patterns within visuo‐vestibular and anxiety‐related systems during simulated vertical self‐motion. Hum Brain Mapp 38:715–726, 2017.

Individual differences in depression are associated with abnormal function of the limbic system in multiple sclerosis patients

Background: Depression is common in patients with multiple sclerosis (MS), although the brain mechanisms of this psychiatric condition in MS are poorly understood. Specifically, it remains to be determined whether depression in MS is related to altered activity and functional connectivity patterns within limbic circuits. Methods: Seventy-seven MS patients with variable levels of depression (as assessed via the Beck Depression Inventory) underwent functional magnetic resonance imaging while performing an emotional processing task. To conduct the functional connectivity analyses, the bilateral amygdala and hippocampus, two areas critically involved in the pathophysiology of depression, were chosen as ‘seed’ regions. Multiple regression models were used to assess how depression in MS patients was correlated with the activity and functional connectivity patterns within the limbic system. Results: Depression scores in MS patients were negatively correlated: (1) with the activity in the subgenual cingulate cortex; (2) with the functional connectivity between the hippocampus and orbitofrontal cortex as well as the dorsolateral prefrontal cortex, and (3) with the functional connectivity between the amygdala and dorsolateral prefrontal cortex. Conclusions: Our study showed that individual differences in depression in MS patients were significantly associated with altered regional activity and functional connectivity patterns within the limbic system.

Fully Automated Segmentation of the Pons and Midbrain Using Human T1 MR Brain Images

Purpose This paper describes a novel method to automatically segment the human brainstem into midbrain and pons, called LABS: Landmark-based Automated Brainstem Segmentation. LABS processes high-resolution structural magnetic resonance images (MRIs) according to a revised landmark-based approach integrated with a thresholding method, without manual interaction. Methods This method was first tested on morphological T1-weighted MRIs of 30 healthy subjects. Its reliability was further confirmed by including neurological patients (with Alzheimers Disease) from the ADNI repository, in whom the presence of volumetric loss within the brainstem had been previously described. Segmentation accuracies were evaluated against expert-drawn manual delineation. To evaluate the quality of LABS segmentation we used volumetric, spatial overlap and distance-based metrics. Results The comparison between the quantitative measurements provided by LABS against manual segmentations revealed excellent results in healthy controls when considering either the midbrain (DICE measures higher that 0.9; Volume ratio around 1 and Hausdorff distance around 3) or the pons (DICE measures around 0.93; Volume ratio ranging 1.024–1.05 and Hausdorff distance around 2). Similar performances were detected for AD patients considering segmentation of the pons (DICE measures higher that 0.93; Volume ratio ranging from 0.97–0.98 and Hausdorff distance ranging 1.07–1.33), while LABS performed lower for the midbrain (DICE measures ranging 0.86–0.88; Volume ratio around 0.95 and Hausdorff distance ranging 1.71–2.15). Conclusions Our study represents the first attempt to validate a new fully automated method for in vivo segmentation of two anatomically complex brainstem subregions. We retain that our method might represent a useful tool for future applications in clinical practice.